review article remote sensing of soil...

Hindawi Publishing CorporationISRN Soil ScienceVolume 2013 Article ID 424178 33 pageshttpdxdoiorg1011552013424178

Review ArticleRemote Sensing of Soil Moisture

Venkat Lakshmi

Department of Earth and Ocean Sciences University of South Carolina Columbia SC 29208 USA

Correspondence should be addressed to Venkat Lakshmi vlakshmigeolscedu

Received 14 November 2012 Accepted 13 December 2012

Academic Editors G Benckiser J A Entry and Z L He

Copyright copy 2013 Venkat Lakshmi This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Soil moisture is an important variable in land surface hydrology as it controls the amount of water that infiltrates into the soil andreplenishes the water table versus the amount that contributes to surface runoff and to channel flow However observations of soilmoisture at a point scale are very sparse and observing networks are expensive to maintain Satellite sensors can observe large areasbut the spatial resolution of these is dependent on microwave frequency antenna dimensions and height above the earthrsquos surfaceThe higher the sensor the lower the spatial resolution and at low elevations the spacecraft would use more fuel Higher spatialresolution requires larger diameter antennas that in turn require more fuel to maintain in space Given these competing issuesmost passive radiometers have spatial resolutions in 10s of kilometers that are too coarse for catchment hydrology applicationsMost local applications require higher-spatial-resolution soil moisture data Downscaling of the data requires ancillary data andmodel products all of which are used here to develop high-spatial-resolution soil moisture for catchment applications in hydrologyIn this paper the author will outline and explain the methodology for downscaling passive microwave estimation of soil moisture

1 Introduction

Soil moisture is an important variable in land surfacehydrology Soil moisture has very important implicationsfor agriculture ecology wildlife and public health and isprobably (after precipitation) the most important connectionbetween the hydrological cycle and lifemdashanimal plant andhuman

Land surface hydrology is a well-studied portion of theterrestrial water cycle The main variables in land-surfacehydrology are soil moisture surface temperature vegetationprecipitation and streamflow Of these surface temperaturevegetation and precipitation are currently observed usingsatellites and streamflow is routinely observed at in situwatershed locations Soil moisture remains the only variablenot observed (or observed very sparsely) either in situ orvia remote sensing Due to this very reason in the pastdecade satellite soil moisture has been increasingly used inhydrological agricultural and ecological studies due to itsspatial coverage temporal continuity and (now) easiness ofuse

Numerous studies have shown the influence of soil mois-ture on the feedbacks between land-surface and climate that

has a profound influence on the dynamics of the atmosphericboundary layer and a direct relationship to weather andglobal climate [1ndash4] Chang and Wetzel [5] have shown theinfluence of spatial variations of soil moisture and vegetationon the development and intensity of severe storms whereasEngman 1997 [6] demonstrated the ability of soilmoisture toinfluence surface moisture gradients and to partition incom-ing radiative energy into sensible and latent heat In large-scale modeling the soil moisture and surface temperatureare key variables in deciding the depth of the planetaryboundary layer and circulation and wind patterns [7ndash9] Ithas been demonstrated that the assimilation of soil moistureobservations in hydrologic models can improve the accuracyof estimated hydrological variables such as evaporationsurface temperature and root-zone soil moisture [10ndash12]Thevarious atmospheric processes that affect the land surface andin turn the influence of the land surface on the atmosphereneed to be clearly quantified In order to accomplish thesetasks it is necessary to intimately understand the relationshipof soil moisture to these phenomena on small and largespatial scales Unfortunately we are limited in our abilityto completely observe large-scale hydrologic land-surfaceinteractions Satellite and aircraft remote sensing enable us to

2 ISRN Soil Science

estimate large-scale soilmoisture for the purpose ofmodelingthe interactions between land and atmosphere helping us tomodel weather and climate with higher accuracy

Recognizing the need for soil moisture observationson large spatial scales for continental scale hydrologicalmodeling investigators in the 1980s used the special sensormicrowave Imager (SSMI) [13] and scanning multichan-nel microwave radiometer [14] data sets The SMMR datahas been used to study soil moisture retrievals sensitivityand scaling on continental scales [15ndash17] The SSMI [1518] has been used in catchment scale studies [19] and incontinental scale studies in conjunction with a hydrologicalmodel [20ndash22] More recently successful retrieval has beencarried out using several missions including WindSat [23]tropical rainfall measuring mission (TRMM) microwaveimager (TMI) [24] Recently a promising soil moisture dataset has been developed jointly by researchers of NASAGoddard Space Flight Center and the Vrije UniversiteitAmsterdam [25] It utilizes C-band AMSR-E microwavebrightness temperatures in a Land Parameter RetrievalModel (LPRM) to obtain soil moisture This product hasbeen tested in a series of validation studies (eg [26ndash28])and shows high correlations with field observations [29]The Global Change Observation Mission-Water (GCOM-W) launched by the Japanese Space Agency is now pro-viding us with microwave data sets of the land surface[30]

Active and passive microwave remote sensing providesa unique capability to obtain observations of soil moistureat global and regional scales that help satisfy the scienceand application needs for hydrology [31ndash33] The emissiveand scattering characteristics of soil surface depend on soilmoisture among other variables that is surface temperaturesurface roughness and vegetation The electromagneticresponse of the land surface is modified by soil moisture andmodulated by surface roughness vegetation canopy effectsand interaction with the atmosphere before being received bya sensor These ancillary (non-soil-moisture) effects increaseat higher frequencies making low-frequency observationsdesirable for observation of soil moisture [34 35] Longerwavelengths also sense deeper soil layers (2ndash5 cm) at the L-band the penetration depth being of the order of one tenth ofthe wavelength [36] Retrieval of soil moisture using ground-based or aircraft-mounted radiometer operating at the L-band has been demonstrated in several prior studies [37ndash42]

An important experiment for investigating the capabilityof the PALS (passive and active L-and S-bands radiome-terradar) was conducted in the Southern Great Plainsregion of United States in July 1999 The SGP99 experimentsincluded bare pasture and agricultural crop surface coverwith field averaged vegetation water contents mainly in the0ndash25 kgmminus2 range Studies based on the SGP99 experimentsexhibited varying soil moisture retrieval potential with a 2-3 accuracy using passive channels and 2ndash5 accuracy usingthe active channels of the PALS instrument [40 42] Therewas a need to conduct similar studies under higher vegetationwater contents in order to evaluate the performance of soilmoisture retrieval algorithms under these conditions

The Soil Moisture Experiments in 2002 SMEX02 wereconducted in Iowa over a one-month period between mid-June and mid-July 2002 A major focus of SMEX02 wasextension of instrument observations and algorithms tomorechallenging vegetation conditions and understanding theimplications on soil moisture retrieval In situ measure-ments of gravimetric soil moisture soil temperature soilbulk density and vegetation water content were carried outcoincidently with PALS observations in active and passivechannels This study evaluated the performance of existingalgorithms andmodels for soil moisture retrieval using activeand passivemeasurements under themoderately high to veryhigh vegetation water content conditions

Field scale validation networks exist in Oklahoma [43ndash45] Illinois [46] and the USDA operational SCAN (SoilClimate Analysis Network) [47] Soil observing networkshave their challenges especially when used to validate satellitedata sets [48]

The NASA soil moisture active passive (SMAP) mission[53] is set for launch in 2014 SMAP will utilize a very largeantenna and combined radiometerradar measurements toprovide soil moisture at higher resolutions than radiometersalone can currently achieve SMAP [53] consists of bothpassive and activemicrowave sensorsThe passive radiometerwill have a nominal spatial resolution of 36 km and the activeradar will have a resolution of 1 km The active microwaveremote sensing data can provide a higher spatial resolu-tion observation of backscatter than those obtained froma radiometer (order of magnitude radiometer sim40 km andradar sim1 km or better) Radar data are more strongly affectedby local roughness microscale topography and vegetationthan a radiometer meaning that it is difficult to invertbackscatter to soil moisture accurately thus limiting thedevelopment of such algorithmsTherefore it can be difficultto use radar data alone SMAP will use high-resolution radarobservations to disaggregate coarse resolution radiometerobservations to produce a soil moisture product at 3 km reso-lution The soil moisture has been retrieved from radiometerdata successfully using various sensors and platforms andthese retrieval algorithms have an established heritage [3154]

There have been methods integrating the use of activesensors that have a higher spatial resolution to downscalepassive microwave soil moisture retrievals [55ndash57] Recentstudies have addressed the soil moisture downscaling prob-lem using MODIS sensor derived temperature vegetationand other surface ground variables The major publicationsin this area of study include the following (i) A methodbased on a ldquouniversal trianglerdquo concept was used to retrievesoil moisture from Normalized Difference Vegetation Index(NDVI) and land surface temperature (LST) data [50] (ii)A relationship between fractional vegetation cover and soilevaporative efficiency was explored for catchment studies inSoutheastern Australia by Merlin et al 2010 [49] while Mer-lin et al 2008 [51] developed a simple method to downscalesoil moisture by using two soil moisture indexes evaporativefraction (EF) and the actual EF (AEF) [58] (iii) A sequentialmodel which used MODIS as well as ASTER (AdvancedScanningThermal Emission andReflection Radiometer) data

ISRN Soil Science 3

Table 1 Studies on downscaling soil moisture using various remote sensing and modeling techniques [41]

Author Methodology Time and region Result

Merlin et al [49]Based on the relationshipbetween soil evaporativeefficiency and soil moisture

NAFE 2006 (Oct-Nov)Yanco SoutheasternAustralia

Mean correlation slopebetween simulated andmeasured data is 094 themost accuracy with anerror of 0012

Piles et al [50] Build model between LSTNDVI and soil moisture

Jan-Feb 2010Murrumbidgee catchmentYanco SoutheasternAustralia

119877

2

is between 014sim021 andRMSE 09sim017

Merlin et al [51]

Downscaling algorithm isderived fromMODIS andphysical-based soilevaporative efficiencymodel

NAFE 2006 (Oct-Nov)Murrumbidgee catchmentYanco SoutheasternAustralia

Overall RMSE is between14sim18 vv

Merlin et al [51] Based on two soil moistureindices EF and AEF

June and August 1990(Monsoonrsquo 90 experiment)USDA-ARS WGEW insoutheastern Arizona

Total accuracy is 3 vol forEF and 2 vol for AEF andcorrelation coefficient is066sim079 for EF and071sim081 for AEF

Merlin et al [52] Sequential modelNAFE 2006 (Oct-Nov)Yanco SoutheasternAustralia

RMSE is minus0062 volvoland the bias is 0045volvol

was proposed for downscaling soil moisture [52 59 60]Table 1 lists these studies the methods and significant resultsof the soil moisture downscaling

This paper is organized as follows Section 2 outlinesthe theory of passive microwave radiative transfer Section 3explains results from the Soil Moisture Experiment 2002(SMEX02) using aircraft-based passive and active sensorsSection 4 describes two methods used for disaggregation ofsoil moisture (1) use of active sensors to detect change insoil moisture at a finer scale and use that information forconstruction of higher spatial resolution estimates of soilmoisture and (2) use of visible and near infrared satelliteobservations to disaggregate passive microwave satellite soilmoistures Section 5 discusses the future of remote sensing ofsoil moisture

2 The Radiative Transfer Model

The earthrsquos surface as seen by a spaceborne or airborneradiometer may include bare or vegetated soil varyingamounts of roughness similar or varying soil types andvariation in the soil moisture content

Any model of radiative transfer from the earthrsquos surfacemust incorporate the effects of these factors observed bright-ness temperatures The radiative transfer model describedhere begins with the bare soil emissivity and then is modifiedfor roughness and vegetation Most of the studies using themodel have been done in the 14ndash66GHz range Atmosphericeffects on the brightness temperatures and the effects ofvolume scattering are not considered as they are negligibleat these frequencies (14ndash66GHz)

21 Emissivity of a Smooth Surface The relationship betweenthe brightness temperature 119879

119861

of a radiating body and itsthermodynamic temperature 119879

119904

is given by the expression

119879

119861

= 119890119879

119904

(1)

where 119890 is the emissivity of the body 119879119861

expressed in KelvinsThe emissivity is related to the reflectivity 119903 of the surface by

119890 = 1 minus 119903 (2)

For a smooth surface and a medium of uniform dielectricconstant the expressions for reflectivity at horizontal andvertical polarizations may be derived from electromagnetictheory [1] as

119903

119907

=

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

120576

119903

cos 120579 minus radic120576119903

minus sin2120579

120576

119903

cos 120579 + radic120576119903

minus sin2120579

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

2

119903

ℎ

=

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

cos 120579 minus radic120576119903

minus sin2120579

cos 120579 + radic120576119903

minus sin2120579

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

2

(3)

where 120579 is the incidence angle and 120576119903

is the complex dielectricconstant of the medium

22 Emissivity of a Bare Smooth Soil Water has a muchhigher dielectric constant compared to soil An increase inthe soil water content increases both the real and imaginaryparts of the dielectric constant of the soil-water mix Thedielectric properties of wet soils have been studied by several

4 ISRN Soil Science

investigators [61 62] (eg [63 64]) The texture of the soilalso plays an important role in determining its dielectricconstant The dielectric constant for wet soil is evaluatedusing an empirical mixing model from Fang et al [61] Forthe purposes of this study the moisture content of the soil isassumed to be uniform to the penetration depth of the sensorand the effects of nonuniformity of moisture with depth arenot considered

23 Effect of Surface Roughness Surface roughness causes theemissivity of natural surfaces to be somewhat higher [1 3565 66] This is attributed mainly to the increased surfacearea of the emitting surface A semiempirical expression forrough surface reflectivity from Ni-Meister et al [12] is usedto account for surface roughness

119903

119901

= [119876119903

0119902

+ (1 minus 119876) 119903

0119901

] exp (minusℎ) (4)

where 1199030119902

and 1199030119901

are the reflectivities the medium wouldhave if the surface were smooth The expression utilizes twoparameters which are dependent on the surface conditions119876 is the polarization mixing parameter and ℎ is the heightparameter These two parameters depend on the frequencylook angle of the sensor and the roughness of the surface andhave to be determined experimentally

The values of 119876 and ℎ are useful for fitting and modelingexperimental data but recent theoretical calculations haveindicated that 119876 and ℎ are not directly related to theparameters rms height and horizontal correlation lengthmeasured in the field and used to characterize rough surfacereflectivity [67]

24 Effect of Vegetation The presence of vegetation canopyin natural areas and crop canopy in agricultural fields has asignificant effect on the remotely sensed microwave emissionfrom soils [68 69] The sensitivity of measured microwaveemission in vegetation-covered fields will be different frombare fields Vegetation is modeled as a single homogenouslayer above the soil The brightness temperature 119879119901

119861

corre-sponding to polarization119901 vertical (119907) or horizontal (ℎ) oversuch a dual vegetation-soil layer is given by Das et al [57]

119879

119901

119861

= 119890

119901

119879

119904

exp (minus120591) + 119879119862

[1 minus exp (minus120591)] [1 + 119903119901

exp (minus120591)] (5)

where 119879119862

is the vegetation temperature 119879119904

is the soil tem-perature 120591 is the vegetation opacity and 119890

119901

and 119903119901

are thesoil emissivity and reflectivity respectively The value of 120591is dependent on frequency vegetation type and vegetationwater content The relationship between 120591 and the vegetationwater content119882

119890

can be described by the following equationfrom [16 35]

119903 =

119887119882

119890

cos 120579

(6)

where 119887 is a function of canopy type polarization andwavelength and cos 120579 accounts for the nonverticalslant paththrough the vegetation

Figure 1 TheWalnut Creek watershed region and PALS flight linesare shown by the blue lines [41]

The land surface as seen by the satellite sensor is aheterogeneous combination of vegetated and bare areas Thevegetated and bare areas have to be disaggregated to findthe proportion of the radiation that reaches the sensor fromthe different types of land cover The land surface can bedisaggregated into a completely shadowed fraction119872 and abare soil fraction119872 by utilizing the leaf area index (LAI) Leafarea index (LAI) defines an important structural property ofa plant canopy the number of equivalent layers of leaves thevegetation possesses relative to a unit ground area

119872 = 1 minus 119890

minus120583LAI (7)

where 120583 is the extinction coefficient The proportion ofmicrowave brightness temperatures contributed by bare soilsand vegetated regions within a certain area can be calculatedusing

119879

119861

= 119872119879

canopy119861

+ (1 minus119872)119879

bare119861

(8)

where119879canopy119861

is from (5) and119879bare119861

corresponds to brightnesstemperature of bare soil ((1) with emissivity corresponding tobare soil)This gives us ability tomodelmicrowave brightnesstemperatures that would be detected by a sensor

3 Results from the SMEX02 Field Experiment

31 SMEX02 Experiment The Soil Moisture Experiments in2002 (SMEX02) were conducted in Iowa between June 25 andJuly 12th 2002 The study site chosen was Walnut Creek asmall watershed in Iowa This watershed has been studiedextensively by the USDA and hence was well instrumentedfor in situ sampling of hydrologic parameters The terrainis undulating and the land lover type for the watershedregion is primarily agricultural with corn and soybeansbeing the major crops Figure 1 shows a map of the studyregion indicating the location of the data collection sitesand the layout of the aircraft flight lines The experimentswere conducted from 25 June to 12th July 2002 duringwhich the soybean fields grew from essentially bare soilsto vegetation water content of 1ndash15 kgm2 while the cornfields grew from 2-3 kgm2 to 4-5 kgm2 (Figures 2 and 3)SMEX02 provided unique conditions of high initial biomasscontent and significant change in biomass over the courseof the experiment The fields selected for in situ sampling

ISRN Soil Science 5

Table 2 Sensitivity of radiometer channel to 0ndash6 cm layer gravimet-ric soil moisture evaluated over corn and soy fields respectivelyTheLH channel has the maximum sensitivity with the sensitivity beingmuch greater over soy fields than corn fields [41]

Period LH LV SH SVCorn

176ndash178 minus0386 minus0242 minus0131 minus0076186-187 minus0831 minus0284 minus0644 minus0070187-188 minus0316 minus0165 minus0164 minus0124188-189 minus0357 minus0132 minus0132 minus0084

Soy176ndash178 minus1182 minus0333 0183 minus0054186-187 minus1013 minus0426 0787 minus0425187-188 minus1117 minus0409 minus0379 minus0133188-189 minus1050 minus0848 minus1047 minus0665

Figure 2 Field WC05 on July 8 (VWC sim 48 kgm2) [41]


were approximately 800m times 800m and the sampling pointswere distributed throughout the field to take into accountthe variations in soil types and therefore soil moisture withineach field

The PALS instrument was flown over the SMEX02 regionon June 25 27 and July 1st 2nd 5 6 7 and 8 2002(corresponding to DOY 176 178 182 183 186 187 188 and189 resp) Initial soil conditions were dry but scatteredthunderstorms occurred between July 4 and 6 enabling awetting and subsequent dry down to be observed The PALSradiometer and radar provided simultaneous observations of

horizontally and vertically polarized L- and S-bands bright-ness temperatures radar backscatter measured in VV HHand VH configurations and nadir-looking thermal infraredsurface temperature The instrument was flown on a C-130(velocity sim 70ms) aircraft at a nominal altitude of sim3500 feetwith the angle of incidence on the surface being sim45 degreesIn this configuration the instantaneous 3 dB footprint on thesurface was 330m times 470m The instrument thus sampleda single line footprint track along the flight path Aircraftlocation and navigation data and downward looking thermalinfrared (IR) temperatures were also recorded in addition tothe radiometer and radar measurements

32 Sensitivity Sensitivity for the passive sensor is evaluatedas the ratio of the change in emissivity to the change ingravimetric soil moisture Δ119890Δ119898

119892

and as the change inradar backscatter to the change in percentage gravimetric soilmoisture Δ120590Δ(119898

119892

) for the active sensor The estimationof emissivity was carried out by dividing the brightnesstemperature in a channel by the surface temperature Sen-sitivity of the radiometer is expected to be negative asan increase in soil moisture results in a decrease in theemissivity while the sensitivity of the radar is supposed tobe positive as the value of backscatter increases with increasein soil moisture Overlying vegetation tends to attenuate theradiometric response of soil thereby reducing sensitivity ofthe microwave sensor to soil moisture the effect of which isseen in the reduced sensitivity over corn fields as compared tosoy fields Someof the sensitivity values presented in the studyhave positive values for radiometer channels and negativevalues for radar channels indicating that overlying vegetationcompletely mask sout the effect of soil moisture on observedbrightness temperatures and backscatter coefficients Thesemeasures of sensitivity allow an intercomparison betweendifferent channels of the radar or the radiometer

There was major precipitation event in the watershedregion on July 7 which provided around 24mm of rain onthe SMEX02 study region Sensitivity computation was doneby averaging PALS observations in a particular channel forcorn and soy fields separately and comparing the changein averaged PALS observations to the change in the 0ndash6 cm layer soil moisture before and after the precipitationevent The results have been presented in Tables 2 and 3As expected the L band horizontal polarization channelhad the maximum sensitivity among passive channels witha maximum sensitivity of 0831 over corn fields and 1182over soy fields For the radar channels the L band verticallycopolarized backscatter was most sensitive to soil moisturewith sensitivities of 0421 for corn and 0639 for soy fieldsThe S-band was less sensitive to soil moisture than the Lband with the maximum sensitivities for passive and activePALS observations being 0644 (SH) and 012 (SVV) overcorn fields and 1047 (SH) and 0380 (SVV) over soy fieldsThese findings illustrate the lower capability of the S-bandto penetrate denser vegetation canopies as in case of cornfields as opposed to soybean fields which had significantlylower vegetation water content In general the PALS sensorexhibited greater sensitivity over the less vegetated soy fields

6 ISRN Soil Science

Table 3 Sensitivity of radar channel to 0ndash6 cm layer gravimetric soilmoisture evaluated over corn and soy fields L band vertically copolarizedchannel is seen to be most sensitive to soil moisture and the sensitivity is seen to be higher for soy fields as compared to corn fields A numberof negative sensitivity values during the dry period DOY 176ndash178 indicate that the contribution of soil moisture to the radar backscatter wasmasked out by vegetation and surface roughness effects [41]

Period lhh lvv lvh shh svv svhCorn

176ndash178 0237 0115 minus0214 0138 minus0173 0051187-188 0222 0302 0199 0114 0120 0122188-189 0309 0421 0374 0078 0103 0113

Soy176ndash178 0019 minus0114 0329 minus0001 minus0521 minus0086187-188 0454 0639 0360 0387 0380 0324188-189 0625 0504 0666 0335 0105 0219Active channels (sensitivity = (Δ120590Δ119898

119892

)lowast 001)

Table 4 Correlations between PALS horizontal polarization brightness temperatures and soil moisture in the 0-1 cm and 0ndash6 cm layersCorrelations generally reduce with increasing vegetation water content The 25 kgm2

lt VWC lt 35 kgm2 and 10 kgm2lt VWC lt

25 kgm2 classes consist mostly of measurements made in the corn fields for the dry days of June 25th and 26th when the contributionof radiation from soil is masked out by the overlying vegetation and the coefficient of correlation is seen to be positive [41]

VWC (kgm2) No of points GSM (0-1 cm) GSM (0ndash6 cm)119877 (LH) 119877 (SH) 119877 (LH) 119877 (SH)

VWC lt 10 52 minus0881 minus0885 minus0808 minus084610 lt VWC lt 25 6 0142 0278 0420 056225 lt VWC lt 35 21 minus0094 minus0197 0258 016135 lt VWC lt 45 13 minus0950 minus0863 minus0847 minus0833VWC gt 45 48 minus0690 minus0641 minus0676 minus0626

(VWC sim 10 Kgm2) in comparison to the cornfields (VWCsim 35 Kgm2) across all channels These findings supportprevious studies showing the LH channel to bemore sensitiveto near-surface soil moisture than LV SH or SV and the LVVchannel to bemore sensitive to soilmoisture than other activechannels [36]

33 Statistical Analysis Numerous prior studies have focusedon either regression between radiometer or radar observa-tions and in situ observations of surface soil moisture [7172] under conditions of low vegetation water content Therelationship between soil moisture and microwave emissionis nearly linear The present study evaluates the performanceof linear regression technique for soil moisture estima-tion under the conditions of high vegetation water contentencountered during the SMEX02 campaign

Soil moisture retrieval was carried out by a simplemultilinear regression procedure As the best correlationbetween brightness temperatures and soil moisture wasgiven by a band combination of LH LV and SH bandsa multilinear regression was done using two combinationsof PALS channels (LH LV and SH) and (LH SH) Soilmoisture retrieval was also carried out using the brightnesstemperatures from a single LH channel Individual observa-tions were classified into five subclasses depending on thevegetation water content Table 4 presents the correlationcoefficients (119877) obtained from a linear regression applied to

the colocated data for all available days of PALS observa-tions Significant correlation was seen between the L bandhorizontal polarization brightness temperature and in situsoil moisture for three of the subclasses with a negativevalue indicating that the brightness temperature decreases assoil moisture increases Regression equations were developedfor each class using 2-3 days of observations (calibrationperiod) and the soil moisture was retrieved for all otherdays (validation period) using these (calibration) equationsAverage root mean square error (RMSE) was computed forthe soilmoisture retrieval in each case Tables 5 and 10 presentthe errors associated with retrieval of soil moisture fromthe LH band and the band combination LH LV and SHThe retrieval errors reduce significantly when multichannelregression retrieval is done especially in the case of thehighest vegetationwater content class (VWC gt 45) where thelowest retrieval error of 0045 gg (gravimetric soil moisture)is obtained for the band combination LH LV and SH ascompared to 0062 gg for retrieval from the LH band onlySimilarly retrieval error for the class with VWC lt 1 kgm2is also the least in case of LH LV and SV band combinationthe error being 0034 gg As expected the retrieval accuraciesdecrease with increase in vegetation water content indicatingthat the sensor becomes less sensitive to soil moisture as thevegetation water content increases

Retrieval of soil moisture was also done by employinglinear or multiple regressions between the colocated radar

ISRN Soil Science 7

Table 5 Root mean square errors associated with soil moisture retrieval from the PALS LH channel by the statistical regression techniqueNote that the retrieval errors are greatest for the VWC gt 45 kgm2 class [41]

Calibration days Note VWC lt 10 1 lt VWC lt 25 25 lt VWC lt 35 35 lt VWC lt 45 VWC gt 45178 189 1 dry 1 wet 00371 00326 00579 00417 00623176 186 188 1 dry 2 wet 00371 00302 00578 00409 00738178 188 189 1 dry 2 wet 00374 00322 00492 00410 00643176 178 189 2 dry 1 wet 00375 00271 00609 00417 00623176 178 187 2 dry 1 wet 00383 00328 00531 00450 00635188 189 2 wet 00403 00337 mdash 00402 00643176 178 2 dry 01459 01228 00774 mdash mdash

Table 6 Correlations between PALS vertically copolarized radarbackscatter and soil moisture in the 0-1 cm layer Correlationsgenerally reduce with increasing vegetation water content The10 kgm2

lt VWC lt 25 kgm2 and 25 kgm2lt VWC lt 35 kgm2

classes consist mostly of measurements made in the corn fieldsfor the dry days of June 25th and 26th when the contribution ofradiation from soil is masked out by the overlying vegetation andthe coefficient of correlation is seen to be negative [41]

VWC (kgm2) No ofpoints

GSM (0-1 cm) GSM (0ndash6 cm)119877

(LVV)119877

(SVV)119877

(LVV)119877

(SVV)VWC lt 10 57 0858 0814 079 07310 lt VWC lt 25 8 0443 0222 017 038925 lt VWC lt 35 28 minus015 minus0146 minus0346 minus043435 lt VWC lt 45 14 0742 0794 0457 0482VWC gt 45 47 0811 0519 0793 0503

backscatter and in situ soil moisture dataset classified on thebasis of vegetation water content Table 6 presents the coef-ficient of correlation (119877) values obtained by single channellinear regression for five different subclasses of vegetationwater content Active channels also give acceptable retrievalerrors with the lowest prediction error for the VWC gt

455 kgm2 class being 00481 gg for the LVV SVV and LHHband combination The retrieval errors associated with soilmoisture retrieval from PALS backscatter coefficients aretabulated in Tables 7 and 8

It is important that the calibrating dataset fairly representsthe conditions encountered during the course of the SMEX02experiments It is seen that the errors increase significantlyif only dry or only wet days are used for calibration Table 8shows that the RMS error increases to 008 gg from 005 ggin case of fields with VWC between 25 and 35 kgm2when 2 dry days were used for developing the regressionequation rather than 1 dry 2 wet or 2 dry and 1 wet daysThe discrepancy seen in the 25 kgm2 lt VWC lt 35 kgm2and 10 kgm2 lt VWC lt 25 kgm2 classes in the form of apositive value of coefficient of correlation for the passive caseand negative value for the active case (Tables 4 and 6) is dueto the fact that these classes consist mostly of measurementsmade in the corn fields for the dry days of June 25 and 26when the contribution of radiation from soil was masked outby the overlying vegetation This effect is also reflected by

the higher soil moisture retrieval errors for this class Soilmoisture retrieval by a multiple channel regression producesmore prediction errors than single channel regression as thevariance in both vegetation and soil moisture is taken intoaccount by a multiple regression

34 ForwardModel for Passive Sensor The forwardmodel forsimulation of brightness temperatures considers a uniformlayer of vegetation overlying the soil surface The dielec-tric behavior of the soil water mixture is modeled usinga semiempirical four-component dielectric-mixing model[64]The upwelling radiation from the land surface observedfrom above the canopy was expressed in terms of radiativebrightness temperature and is described by the radiativetransfer model [69 73 74]

A physical model for passive radiative transfer was usedfor simulation of PALS-observed brightness temperaturesand subsequent soil moisture retrieval In situ measurementsof soil moisture land surface temperature bulk density andvegetation water content were made during the SMEX02experiments The in situ measurements of vegetation watercontent were used to calibrate a model that used a LandsatTM derived parameter NDWI which was used to derivethe vegetation water contents for the SMEX02 region for theentire study period A summary of the parameters that wereused for calibration and soil moisture retrieval from PALS-observed L band brightness temperatures has been presentedin Table 9

Soil surface roughness ℎ polarization mixing parameter119902 and single scattering albedo were not available asmeasuredquantities Polarization mixing was taken as zero and singlescattering albedo was taken as 01 for both vertical andhorizontal polarizations for corn as well as soy canopiesSurface roughnesswas used as a free parameter for calibratingthe model Vegetation opacity was taken to be 0086 for soycanopy and 012 for corn canopy as reported in previousstudies [17] For deriving the optimum values ℎ was variedbetween 0 and 06 separately for corn and soy fields andthe estimation was done on the criteria of minimum rootmean square error between PALS-observed average bright-ness temperature (119879BH + 119879BV)2 in the L band and themodeled average brightness temperature for the L band Theaverage brightness temperatures were normalized by 119879LSTto account for surface temperature contribution Calibration

8 ISRN Soil Science

Table 7 Root mean square errors associated with soil moisture retrieval from the PALS LVV SVV and LHH band combination The errorsgenerally increase with vegetation water content [41]


Table 8 Root mean square errors associated with soil moisture retrieval from the PALS LVV band by the statistical regression technique [41]


Table 9 Summary of parameters input to the radiative transfermodel for simulation of PALS brightness temperatures [41]

(a) Media and sensor parametersVegetation

Single scattering albedo 120596 01Opacity coefficient 119887 0086 (soy) 012 (corn)

Soil

Roughness coefficients ℎ (cm) and 119876 ℎ-calibrated119876 = 0

Bulk density (g cmminus3) in-situSand and clay mass fractions 119904 and 119888 CONUS-SOIL dataset

SensorViewing angle 120579 (deg) 45Frequency 119891 (GHz) 141 269Polarization H V

(b) Media variablesLand surface

Surface soil moisture119898119907

(g cmminus3) In-situVegetation water content 119908

119888

(kgmminus2) Landsat TMSurface temperature 119905 (K) In-situ

was performed for different combinations of 2 or 3 days ofPALS observations out of the 7 days available

The calibrated values of ℎ for each field and in situmeasurements of model soil moisture bulk density surfacetemperature and vegetation water content were used tosimulate horizontal and vertical polarization brightness tem-peratures for the L- and S-bands Gravimetric soil moisturein the 0ndash6 cm layer was used Simulations were run forvarious combinations of calibration days The root meansquare error (RMSE) for simulation of average brightness

L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136

Figure 4 Model-predicted versus PALS-observed L band bright-ness average temperatures The prediction is better at high soilmoisture conditions (lower average brightness temperature) whencontribution of vegetation and roughness is not as significant as thatof soil moisture Model calibrated using PALS and in situ data forJuly 7 and July 8 [41]

temperature in the L band was 71 K and 80 K for the S-band when the calibration was done for DOYrsquos 176 188and 189 Simulation results were better for soy fields with anRMSE of 68 K as opposed to corn fields with an RMSE of72 K for the L band Figures 4 and 5 present the plots forPALS observed versus model-predicted average brightnesstemperature for the L- and S-bands Retrieval of soil moisturewas performed by using a simple retrieval algorithm thatarrives at a soil moisture estimate by minimizing the errorbetween simulated and observed L band average brightnesstemperature normalized with the land surface temperature

ISRN Soil Science 9

Table 10 Root mean square errors (gg) associated with retrieval of soil moisture (gravimetric) by physical modeling of PALS L bandobservations considering the retrieved soil moisture is representative of the in situ 0ndash6 cm layer soil moisture [41]

Calibration days Note vwc lt 1 1 lt vwc lt 25 25 lt vwc lt 35 35 lt vwc lt 45 45 lt vwc178 189 1 dry 1 wet 00375 00343 00287 00327 00439176 186 189 1 dry 2 wet 00404 00310 00365 00504 00436178 188 189 1 dry 2 wet 00475 00338 00309 00271 00398176 178 189 2 dry 1 wet 00398 00297 00230 00519 00518176 178 187 2 dry 1 wet 00442 00042 00252 00661 00465188 189 2 wet 00326 00341 00403 00334 00283176 178 2 dry 00455 00033 00221 00737 00472176 188 189 1 dry 2 wet 00333 00286 00299 00391 00454

S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577

Figure 5 Model-predicted versus PALS-observed S-band averagebrightness temperatures Model calibrated using PALS and in situdata for July 7 and July 8 [41]

0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134

Figure 6 Model-retrieved gravimetric soil moisture (gg) versussoil moisture measured in situ in the 0ndash6 cm soil layer Modelcalibrated using PALS and in situ data for July 7 and July 8 [41]

(119879119861

(modeled) minus 119879119861

(observed)119879LST) where 119879LST is the landsurface temperature in Kelvins Soil moisture retrieval wasdone for various combinations of calibration days and theRMSE for the retrieval was evaluated in each case Table 10presents the errors between in situ soilmoisture in the 0ndash6 cmsoil layer and the model retrieved soil moisture values for

five classes based on vegetation water content The retrievalerrors increase as the vegetation water content increasesbeing around 003 gg for the VWC lt 1 kgm2 fields andgreater than 004 gg for the fields with VWC gt 45 kgm2Figure 6 is a plot of soil moisture retrieved from PALS L bandhorizontal polarization brightness temperatures versus the insitu soil moisture in the 0ndash6 cm soil layer with the calibrationbeing done using DOYrsquos 176 188 and 189

Physical modeling of brightness temperatures proved tobemore accurate than statistical regression techniquewith anoverall soil moisture retrieval accuracy of around 0036 gg ascompared to 005 gg for the statistical regression techniqueError may have been introduced in the soil moisture retrievalprocess due to improper in-situ sampling measurement ofgravimetric soil moisture and bulk density and the assump-tion that single scattering albedo and vegetation opacity areindependent of look angle and polarization Assignment ofa single value of ℎ and 119902 for all fields of a particular croptype is also a source of error and field measurements ofsurface roughness are desirable In the present study botheast-to-west and west-to-east flight lines were considered tomaximize the number of fields observed by PALS on eachday If a field was observed during both the east-to-west andwest-to-east passes the value from the east-to-west flight linewas chosen This also introduces a source of error in boththe statistical regression and physical modeling techniques ofsoil moisture retrieval as the PALS overpass times may notcoincide with the in situ sampling time for soil moisture in aparticular field and data from twoflight linesmay be differentdue to factors such as sun glint

4 Disaggregation of Soil Moisture

41 Radar-Radiometer Method

411 The Importance of Radar for Soil Moisture Radars havea higher spatial resolution than radiometers Retrieval of soilmoisture using radar backscattering coefficients is difficultdue tomore complex signal target interaction associated withmeasured radar backscatter data which is highly influencedby surface roughness and vegetation canopy structure andwater content Several empirical and semiempirical algo-rithms for retrieval of soilmoisture from radar backscatteringcoefficients have been developed but they are valid mostly

10 ISRN Soil Science

in the low vegetation water content conditions [75ndash77]On the other hand the retrieval of soil moisture fromradiometers is well established and has a better accuracy withlimited requirements for ancillary data [78 79] Radiometermeasurements are less sensitive to uncertainty in measure-ment and parameterization of surface roughness and vege-tation canopy interaction However the spatial resolution ofradiometer is much lower compared to radar operating inthe same band An optimal soil moisture retrieval algorithmthat combines the higher spatial resolution of radar withhigher sensitivity of a radiometer might result in improvedsoil moisture products

Temporal evolution of soil moisture can be potentiallymonitored through change detection Change detectionmethods [70] have been implemented as a convenient way todetermine relative soilmoisture or the change in soilmoisture[42 80] Both brightness temperature and radar backscatterchange depend approximately linearly on soil moisture andhence sensitivity can be assumed to be independent ofsoil moisture However quantification of sensitivity requiressoil moisture measurements which is difficult in the caseof radar in the presence of moderate to high vegetationcover It may be possible to estimate the radar sensitivity tosoil moisture by using radiometer-estimated soil moisturemeasurements if the impact of vegetation on sensitivity andspatial heterogeneity issues can be accounted for

This section proposes a simple algorithm that uses higherresolution radar observations along with coarser resolutionradiometer observations to determine the change in soilmoisture at the spatial resolution of radar operation withoutusing any in situ soil moisture measurements The presentstudy simplifies the problem of spatial disaggregation ofsoil moisture by considering that the spatial variability ofbare soil properties (texture roughness) that influence radarsensitivity to soil moisture is not significant and hence thevariability of radar signal within the radiometer footprint isdue to soil moisture and canopy vegetation water contentvariability only

The next section explains the theoretical basis andassumptions behind the algorithm for spatial disaggregationused in this study Section 413 presents the data and themethods that are applied to evaluate the performance of thealgorithm presented in the study Section 414 presents theresults in terms of comparison of in situmeasurements of soilmoisture with the disaggregated estimates obtained from thealgorithm

412 Theory for Change Detection Brightness temperatureand radar backscatter have a nearly linear relationship tosurface soil moisture for uniform vegetation and land surfacecharacteristics The radiative transfer model for estimationof soil moisture from brightness temperature is well estab-lished and needs few ancillary parameters for soil moistureestimation The C-band radiometer AMSR-E has a globalsoil moisture product and future L-band radiometers such asSMOS andHYDROSwill have radiometer-only soil moistureproducts However the radiometer-only soil moisture prod-uct is limited in application by the low spatial resolution of the

radiometer instrument Higher spatial resolution is possiblewith radar soil moisture estimation however estimation ofabsolute soil moisture from radar backscattering coefficientsrequires modeling a complex signal target interaction Evenin empirical and semiempirical studies vegetation canopyand soil parameters may be needed to classify a hetero-geneous target area into subclasses that are fairly uniformin terms of those parameters Several studies based on theapproach of classification and linear parameterization of L-band radar backscatter measurements with respect to soilmoisture within each class have been performed in the past[65 76 81 82]

The approach taken by the present study is changeestimation which takes advantage of the approximately lineardependence of radar backscatter change on soil moisturechange [42] Njoku et al demonstrated the feasibility ofa change detection approach using the PALS radar andradiometer data obtained during the SGP99 campaign ThePALS and in situ soil moisture data were classified into3 different classes based on the vegetation water contentFor each class linear least square fits of PALS brightnesstemperature and radar backscatter to soil moisture weredeveloped The linear relationships were modeled as

119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)

The PALS data used for the SGP99 study had the samefootprint size for both radar and radiometer Hence 119860 119861119862 and 119863 are parameters for each pixel in the coincidentradar and radiometer images and were assumed to primar-ily be functions of surface vegetation and roughness (andtemperature for the passive case) Difference images wereobtained by subtracting the sensor data on the first dayfrom the sensor data on the consecutive days They wereable to calibrate 119862 and 119863 parameters in (10) using 2 days ofradiometer estimates of soil moisture under wet and dry soilconditions Further using 119862 119863 and 1205900

119907119907

they derived radar-estimated soil moisture with satisfactory results Our studyis aimed at estimation of soil moisture change at the spatialresolution of radar by combining radar and radiometer dataThe approach and assumptions are similar to the Njokuet al study however in our case the radar is at higherspatial resolution of 100m as compared to the 400m spatialresolution of the radiometer We assume that the changes invegetation canopy parameters are insignificant as comparedto the change in soil moisture when considering the resultingchange in copolarized radar backscatter given a sufficientlyhigh revisit rate of the sensor over the target Using thisassumption the difference image obtained by subtractingconsecutive radar backscatter images acquired over an areawould be given by

Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901

is the change in copolarized radar backscatter (dB)and Δ119898

119907

is the change in soil moisture The parameter 119863 isexpected to depend on the attenuation characteristics of the

ISRN Soil Science 11

vegetation canopy and the surface roughness characteristicsof the soil surface Results from Friedl et al [62] indicatethat relative sensitivity of the L-band copolarized channelsof radar should depend primarily on the vegetation canopyopacity Relative sensitivity is defined as the ratio of the radarsensitivity in the presence of a vegetation canopy (119863) to thesensitivity if there was only bare soil (119863

0

)

119863

119863

0

= 119891 (120591) (12)

Figure 12 in Du et al shows the variation of the relativesensitivity for a medium rough soil surface having a soybeancanopy The plot suggests that relative sensitivity can beestimated from optical thickness once the canopy type andsurface type are known The vegetation opacity 120591 can beestimated using the (120591 = 119887VWCcos 120579) relationship forvegetation canopies that follow the electrically thin scattererapproximation 119887 is a parameter that depends on vegetationstructure and type 120579 is the incidence angle and VWC isthe vegetation water content which can be estimated oper-ationally using proxies such as NDWI [83] Now combining(11) and (12) we can write

Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)

The bare soil sensitivity 1198630

depends only weakly on soilroughness variability for a given sensor configuration (fre-quency polarization and viewing angle) To substantiate thisfurther the author conducted a simulation in which theIntegral Equation Model [65] was used to generate plots ofvertically copolarized radar backscatter versus soil moisturefor various rootmean square soil roughness values (Figure 7)It is seen in the figure that the line plots for 119904 = 04 cm to 119904 =24 cm can be approximated as a series of parallel lines withthe same slope and different intercepts The result indicatesthat for the L-band surface roughness variability has only asmall effect on the soil moisture sensitivity of radar Nowfrom (13) we obtain

Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0

Let us assume that the radar backscatteris available at a finer resolution of ldquo119909rdquo whereas radiometerdata is available at a coarser resolution of ldquoXrdquo The problemthen is to estimate Δ119898

119907119909

that is the change in soil moisturefrom time step 119905

0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050

+ Δ119905 using (12) The change in the soil moisture at thecoarser spatial resolution (X) can be evaluated as the sum ofall the changes at the finer resolution (119909) that is

Δ119898

119907X =1

119873

sum119898

119907119909

(15)

The summation is over all the ldquo119873rdquo smaller radar footprintswithin the larger radiometer footprint and Δ119898

119907X is thechange in soil moisture as measured by the radiometer at thelower spatial resolution Δ119898

119907119909

is the change in soil moisture

asmeasured by the radar at the higher spatial resolution givenby (12) Combining (12) and (13) leads to

Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780

will be the same for all the radar pixelswithin a radiometer pixel given uniform vegetation char-acteristics within the radiometer footprint that is 119891(120591) isthe same for all radar pixels within the radiometer pixelsThe author recognize the fact that the spatial variability of119891(120591) will not be low in a real world setting However in thecase of the SMEX02 experiments each radiometer footprintwas contained completely within an agricultural field withfairly uniform vegetation characteristics In the present studywe do not attempt to model for the vegetation canopyvariability within the radiometer footprint 119878

0

is evaluatedfor each radiometer pixel by dividing the summation ofchange in radar backscattering coefficients with the changein radiometer scale soil moisture The summation is for allradar pixels that lie within the radiometer pixel

119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0

for a particular radiometer pixel can be resampled to theradar spatial resolution and for each radar pixel within theradiometer pixel we write the change in soil moisture atresolution ldquo119909rdquo as

Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)

Another important issue in the low spatial variability of 1198780

assumption is the implicit assumption that multiple days ofradar data were obtained over the region at the same angle ofincidence At different incidence angles corresponding AIR-SAR pixels will exhibit different sensitivity to soil moistureDuring SMEX02 the near and far look angles were 228∘ and713∘ and July 5 220∘ and 712∘ for July 7 and 241∘ and 713∘for July 8 This indicates that AIRSAR acquired data overeach of the fields at approximately similar incidence anglesfor the three days The variation of incidence angles betweentwo fields is not important as the relative change in radarbackscatter is considered with the relative change in the soilmoisture on a field-wise basis The low-resolution sensitivityparameter 119878

0

is derived separately for each field As a resultonly the variation of incidence angle within each field will beimportant and this variation is small Within the dimensionsof the PALS footprint this variation in AIRSAR incidentangles will be small and its effect on sensitivity negligible

Accurate estimation of the change in soil moisture at thelower spatial resolution (Δ119898

119907X) is crucial to the accuracyof computed radar sensitivity to soil moisture (15) andhence the accuracy of the high-resolution soil moisturechange estimated by the approach presented in this paperIn an operational setting Δ119898

119907X can be estimated fromsingle-or multichannel passive remote sensing observationsEstimation of soil moisture from passive remote sensing


01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01

Figure 7 Simulation of L band horizontally copolarized radarbackscattering coefficients using the integral equation model [70]for various values of volumetric soil moisture 119898

119907

and rms surfaceroughness 119904 (cm) Surface correlation length has been taken as 8 cm sand = 30 and clay = 30 For various roughness valuesmoistureversus backscatter curves can be approximated as straight lines withthe same slope L band vertically copolarized radar backscatteringcoefficients show a similar behaviour too [55]

has been studied in several prior works and the author donot attempt to retrieve soil moisture from PALS radiometerdata used in this study A previous study [41] demonstratedPALS radiometer to be sensitive to soil moisture during theSMEX02 experiments and retrieval of soil moisture fromPALS L-band brightness temperature data was done withestimation errors of approximately 4 In the current studyin situ soil moisture measurements have been upscaled to400m resolution by averaging and randomly varying noiseis added to the upscaled soil moisture values This provides asimple way to simulate soil moisture retrievals using passiveremote sensing PALS data are used to demonstrate thesensitivity of AIRSAR L-band copolarized channels to soilmoisture only

413 Data from SMEX02 The algorithm discussed abovewas tested on data obtained from the Soil Moisture Exper-iments in 2002 (SMEX02) The SMEX02 campaign wasconducted in Walnut Creek a small watershed in Iowaover a one-month period between mid-June and mid-July2002 An extensive dataset of in situ measurements of soilmoisture (0ndash6 cm soil layer) soil temperature (surface 5 cmdepth) soil bulk density and vegetation water content wascollected Aircraft-mounted instrumentsmdashthe passive andactive L- and S-band sensor (PALS) and the NASAJPLairborne synthetic aperture radar (AIRSAR)mdashwere flownwith supporting ground-sampling dataThePALS instrumentwas flown over the SMEX02 region on June 25 27 and July1st 2nd 5 6 7 and 8 2002 [84 85] PALS radiometer andradar provided simultaneous observations of horizontallyand vertically polarized L- and S-bands brightness tem-peratures radar backscatter measured in VV HH and VHconfigurations and thermal infrared surface temperature ata resolution of sim400m The AIRSAR instrument has P- L-and C-bands with HV dual microstrip polarizations and

0

1

2

3

4

5

6

0 01 02 03Change in soil moisture (cccc)

Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057

Figure 8 Plot of change in AIRSAR LVV backscatter at 800mresolution versus change in in situ volumetric soil moisture at a800m resolution for the periods July 5 to July 7 and July 5 to July8 [55]

spatial resolutions of 5m in slant range and 1m in azimuth[86] AIRSAR instrument was flown on July 1st 5 7 8 and9 The algorithm proposed in this study needs simultaneousobservations of the radar and radiometer As the PALS spatialcoverage of the watershed on July 1st was partial the datasets for PALS and AIRSAR for July 5 July 7 and July 8were used AIRSAR data used in this study was L band HHand VV polarization and provided at a spatial resolution of30m after processing The AIRSAR images were geolocatedby registration to a Landsat TM7 image The ground-basedsoil moisture data used in this study was the volumetric soilmoisture measured using a theta probe at 14 locations ineach field site for all the 31 fields There were 10 soy fieldsand 21 corn fields The representative area for each field sitewas 800 times 800m2 Seven measurements of volumetric soilmoisture made along each of two parallel transects 600m inlength and placed 400m apart Higher-resolution estimatesof in situ soil moisture for each field were estimated by aninverse-distance-weighted spatial interpolation using all the14 measurements for each field and a cell size of 100m

414 Results fromRadar-Radiometer As an initial evaluationof radar sensitivity to soil moisture the AIRSAR L bandvertically copolarized backscattering coefficients were aggre-gated to 800m and then collocated with the field sites thatwere sampled for 0ndash6 cm volumetric soil moisture Figure 8presents a plot of the change in 800mresolutionfield averagesof AIRSAR LVV backscattering coefficient and soil moisturefor the periods July 5 to July 7 and July 5 to July 8The changein soilmoisture between July 7 and July 8was not very high Inorder to see a greater change in soil moisture the difference inJuly 5ndashJuly 8 was selected The 1198772 value of 057 indicates thatΔ120590

0

LVV is sensitive to the change in soil moisture even underthe dense vegetation conditions encountered in the SMEX02


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038

Figure 9 Plot of change in AIRSAR LHH backscatter at 100mresolution versus change in in situ volumetric soil moisture at 100mresolution for the periods July 5 to July 7 and July 5 to July 8 [55]

0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039

Figure 10 Plot of change in AIRSAR LVV backscatter at 100mresolution versus change in in situ volumetric soil moisture at 100mresolution for the periods July 5 to July 7 and July 5 to July 8 [55]

experiments with the vegetation water content of corn fieldsbeing around 4-5 kgm2

The sensitivity of the AIRSAR LVV channel to soilmoisture was also analyzed at a higher spatial resolution of100m In Figures 9 and 10 the change in radar backscatter(LHH and LVV resp) is compared to the correspondingchange in gravimetric soil moisture at a resolution of 100mfor the time periods July 5 to July 7 and July 5 to July 8119877

2 values of 038 and 039 are obtained for LHH and LVVrespectively indicating that radar sensitivity to soil moistureis significant at the higher spatial resolution of 100m alsoThe sensitivities are approximately the same for both LHH

and LVV channels with values of 222 and 238 dB(cccc)respectively It should be noted that there are several datapoints in Figures 8 9 and 10 with negative backscatter changecorresponding to positive change in soil moisture At the800m spatial resolution (Figure 8) we see data points with anegative change in the range 0 tominus1 dB for change inmoisturein the range 0 to 005 cccc At the 100m spatial resolution(Figures 9 and 10) several data points undergo a negativechange in the range 0 to minus2 dB for change in moisture inthe range 0 to 01 cccc The authors believe that this effectis primarily due to change in vegetation water content Forexample in Figure 8 pixels increased in moisture from July5 and July 7 by a small amount (lt005) but still underwenta negative change in backscatter as the vegetation watercontent increased from July 5 to July 7 causing a greaterattenuation of radar backscatter on July 7 as compared to July5 to resulting in a negative net change in radar backscattereven though the moisture increased Soil roughness may alsochange after a rainfall event causing the soil surface to reducein roughness and result in lower radar backscatter valuesafter the rainfall event The assumption made by the authorthat vegetation and soil roughness do not change betweenconsecutive observations is weak but it also simplifies theproblem significantly with satisfactory results in terms ofestimated soil moisture change

It was shown in a previous study that for the SMEX02field experiment PALS LV channel brightness temperatureswere well correlated to soil moisture [41] A further demon-stration of the AIRSAR LVV channel sensitivity to changein soil moisture is done by comparison of the change inPALS LV channel brightness temperatures to the change inAIRSAR LVV channel backscattering coefficients Figure 11presents the difference images produced by the change inAIRSAR LVV backscattering coefficients from July 5 to July7 compared to the change in PALS LV channel brightnesstemperature for the same period The spatial patterns corre-sponding to wet and dry regions in the watershed are verysimilar for both the PALS and AIRSAR difference imagesRegions that became wetter from July 5 to July 7 underwenta reduction in brightness temperature and an increase inbackscattering coefficient (The scales for the two imagesin Figure 11 are hence inverted to represent the positivechange in radar backscatter and negative change in brightnesstemperature with an increase in soil moisture) The cor-relation between PALS LV channel brightness temperaturechange and AIRSAR LVV channel radar backscatter changeis further brought out in Figure 12 Change in AIRSARbackscattering coefficients (LVV) have been aggregated to400m resolution and compared with the change in PALSbrightness temperatures (LV) at its resolution of 400m Anexcellent agreement is seen between the two with an 1198772 valueof 081 indicating that AIRSAR LVV channel has a significantsensitivity to soil moisture

The algorithm discussed in the previous section wasapplied to the 3 days of AIRSAR LVV PALS LV and ground-based soil moisture data 400m resolution estimates of soilmoisture (119898

119907X) were calculated using the in situ measure-ments of soilmoisturewithin each field Asmentioned earlier14 theta probe measurements of soil moisture were made at


each watershed-sampling site that had dimensions of 800mby 800m The in situ measurements were gridded to a100m dimension grid using inverse distance interpolationand then they were upscaled to 400m corresponding to thedimensions of the PALS radiometer footprint A uniformlydistributed random noise of 0ndash0016 gcc was added to theupscaled in situ soil moisture values in order to simulate 4maximum error that would be obtained if the soil moistureestimates were obtained by inverting soil moisture estimatesfrom L-band brightness temperatures using a radiative trans-fer model AIRSAR LVV data was also aggregated to aresolution of 100m and the difference images were usedto compute Δ1205900

119909

where ldquo119909rdquo denotes the lower cell size of100m Using the algorithm discussed in the previous section100m resolution estimates of the change in soil moisture wereobtained for two periods of July 5 to July 7 and July 7 toJuly 8 for each field site Figures 13(a) and 13(b) compareestimated change in soil moisture with measured changein soil moisture at a 100m resolution for the period July5 to July 7 and Figures 14(a) and 14(b) present the samecomparison for the period July 5 to July 8 The root meansquare error for the prediction (RMSE) in both cases is0046 cccc volumetric a major portion of which seems to becontributed by a few outliers It was noted that on removing 7outliers from the plot in Figure 13(a) and 32 outliers from theplot in Figure 14(a) the RMSE improves to 0032 cccc and0024 cccc respectivelyThe outliers seen in Figures 13 and 14are caused by the invalidity of the assumption that vegetationand surface roughness do not change significantly duringconsecutive time steps for few data points For example theencircled data point in Figure 13(a) is observed on fieldWC11where the observed change in radar backscatter (LVV) wasminus1871 dB and with no change in the corresponding change inin situ soil moisture Table 11 presents the observed changein soil moisture and corresponding change in LVV radarbackscatter from July 5 to July 7 for the field WC11 It isseen that although change in soil moisture was low for alldata points the change in corresponding radar backscatterwas not low for all pixels This may be a result of severalfactors such as significant localized change in vegetation orsurface roughness heterogeneity within the 100m pixel suchas present of ponded water within the pixel but not at thesoil moisture sampling location human errors in samplingof soil moisture and errors in geolocation of the AIRSARdata Such pixels are not numerous and hence were notanalyzed in complete detail in the present study Computationof radar sensitivity when the soil moisture change is low isnumerically unstable as Δ119898

119907X appears in the denominatorequation (15) It may be possible to develop an alternateapproach for computing sensitivity where a time series ofradar backscatter and soil moisture observations is used tocompute sensitivity using the lowest and highest soilmoisturevalues observed during a period of say 7ndash10 days duringwhich the change in vegetation and surface roughness canbe assumed to be insignificant Having a greater range of soilmoisture values will lead to a more accurate computation ofradar sensitivity to soil moistureThe suggested approach hasnot been attempted in the current study only 3 days of datawere available

42 Downscaling Using Visible and Near Infrared Satellite

421 Background In this approach we used the relationshipbetween soil moisture and surface temperature modulated byvegetationThis approach has a background with past studiesofMallick et al [87] who used the triangular relationship [6888ndash90] between surface temperature and the vegetation index(TvX) derived from the MODIS Aqua sensor data From thisthey derived the soil wetness index that was converted to soilmoisture at a 1 km scale Minacapilli et al [91] used thermalinfrared observations from an airborne platform to estimatesoilmoisture using the thermal inertia principle for a bare soilfield They found that the estimated soil moisture correlatedvery well with in situ observations Gillies and Carlson [67]devised a method that derived the fractional vegetation andspatial patterns of soil moisture using the AVHRR data setand demonstrated this method in a region of England Inour method the diurnal temperature range (DTR) was used[92] which was affected by vegetation [93] soil moisture andclouds [94]

This section is organized as follows Section 422 pro-vides the list of datasets and the locations of our studySection 423 outlines the downscaling algorithm methodol-ogy In Section 424 we discuss our findings and validationof the results The results and discussion are presented inSection 424

422 Data Oklahoma was selected as the study area dueto the long history of soil moisture research focused onthis region The Oklahoma Mesonet and Little WashitaRiver Experimental Watershed are two long-term in situ soilmoisture networks which provide a solid foundation for soilmoisture remote sensing research (shown in Figure 15) TheLittle Washita has been the location for various soil moisturefield experiments including SGP97 SGP99 and SMEX03[95 96] and has been a key element in satellite validationstudies [97 98] In addition to the ground resources avariety of spaceborne sensors also contributed to this studyDescriptions and maps of the datasets used in this articleare shown in Table 12 and Figure 16 Table 12 lists the spatialresolution and temporal repeat of these sensors and their dataproducts

(1) NLDAS Data The NLDAS (North American Land DataAssimilation System httpldasgsfcnasagovnldas) phase2 hourly mosaic data is used in this study NLDAS is runhourly on a geographical grid with a spatial resolution of18∘ (125 km) The NLDAS-2 data output includes varioussurface variables such as radiation flux surface runoffsurface temperature vegetation indices and soil moisture[99] Soil vegetation and elevation are parameterized usinghigh-resolution datasets (1 km satellite data in the case ofvegetation)The forcing data [100 101] and outputs have beenextensively validated [102ndash104] The soil moisture downscal-ing model in this study utilized two variables surface skintemperature and soil moisture content at 0ndash10 cm depthsThe data used in this study correspond to the closest local


overpass times of Aqua satellite for the Oklahoma regionwhich are approximately 800 and 2000 PM (in UTC time)

(2) AMSR-E Data The Advanced Scanning MicrowaveRadiometer on board the EOS Aqua platform (AMSR-E)collected microwave observations at 6 10 19 37 and 85GHzfrom 2002 to 2011 [105] The AMSR-E instrument pro-vided global passive microwave measurements of terrestrialoceanic and atmospheric parameters for hydrological studiesfrom 2002ndash2011 [105 106] The soil moisture product derivedfrom the AMSR-E sensor on board the Aqua satellite has14∘ (25 km) spatial resolution The estimation of AMSR-Esoil moisture accuracy is approximately 10 and cannot beestimated in the area of vegetation biomass which is greaterthan 15 kgm2 [73]

In this study the AMSR-E soil moisture was estimatedby using the single-channel algorithm (SCA) [97 107] Thesingle-channel algorithm uses the X-band observations ath-polarization (the most sensitive channel) The C-bandobservations cannot be used for land surface applicationsbecause they are significantly affected by manmade radiofrequency interference (RFI) The land surface temperaturewas estimated using the 37GHz v-pol observations AVHRR-derived climatological dataset was used to correct for vegeta-tion effects For matching with other georeferenced datasetsa drop-in-the-bucket method was applied to the AMSR-Edata and it was gridded to a 25 times 25 km EASE grid cell sizeThis method averaged all the AMSR-E points by determiningif their center coordinates were within the borders of aparticular EASE grid cell

(3) MODIS Data The surface temperature data which cor-responded to the local time of 130 and 1330 as well asthe NDVI from MODISAqua were used in this studyMODIS has 36 spectral bands including visible near infraredand thermal infrared spectrum and provides 44 global dataproducts [108]The algorithms to derive theMODIS productsare well established and have been extensively evaluatedincluding NDVI [109 110] LAI [111] land cover classification[62 112] and surface temperature [113] In the current studysurface temperature and NDVI products at two differentspatial resolutions were used for downscaling soil mois-ture The datasets included 1 km daily surface temperature(MYD11A1) 1 km biweekly NDVI (MYD13A2) and 5600mbiweekly Climate Modeling Grid (CMG) NDVI (MYD13C1)In addition the dry down curves of soil moisture duringMay 2004 July 2005 and August 2005 in Oklahoma wereexamined During these three months clear days (due to therequirement of surface temperature in our algorithm) of 1 kmsurface temperature along with low cloud cover were selectedfor the downscaling algorithm application

(4) AVHRR Data For the years 1981ndash2001 prior to thelaunch of the Aqua satellite and the availability of MODISdata the 5 km CMG daily NDVI data from AVHRR sensor(AVH13C1) was used The AVHRR sensor is on board theNOAA satellites including N07 N09 N11 and N14 and pro-vides global and long-term surface ground measurementsDaily AVHRR NDVI data is available between 1981ndash1999(httpltdrnascomnasagovltdrltdrhtml) After 2000 the

Table 11 Change in in-situ soil moisture (cccc) and correspondingchange in radar backscatter (LVV) from July 5th to July 7th for thefield WC11 at a 100m spatial resolution [55]

Field Δ120579 Δ120590

WC11 minus0009 1431WC11 minus0007 0356WC11 minus0039 minus0049WC11 0000 minus1871WC11 minus0004 minus1081WC11 minus0005 minus0546WC11 minus0034 minus0842WC11 minus0011 minus0816WC11 minus0013 0028WC11 minus0001 minus1419

N14 orbit drifted greatly which can degrade the data qualityTherefore the years between 2000ndash2002 were not used in thissoil moisture downscaling exercise

(5) Oklahoma Mesonet Data The Oklahoma Mesonet is anetwork of 120 automated environmentalmonitoring stationswith at least one site in each of the 77 counties of Oklahoma[114] The environmental variables are obtained at intervalsspanning every 5 to 30 minutes depending on the variableThe data quality is verified by a series of automated andman-ual checks via the Oklahoma Climatological Survey [45] Inthis investigation 5 cm soil water content measurement from116 stationswas extracted and geolocated for comparisonwiththe 1 km downscaled AMSR-E and NLDAS soil moisturevalues The locations of the Oklahoma Mesonet stations aredenoted by open yellow circles in Figure 15

(6) Little Washita Watershed DataThe Little Washita Water-shed is located in the southwestern portion of Oklahomaand 20 stations are located within a 25 km by 25 km regionreferred to as the Little Washita MicronetThe watershed soilmoisture estimates from the most reliable stations with theclosest time to the Aqua overpass time were extracted andthen averaged for validation [84 97] The locations of thesestations are denoted by red dots in Figure 15

423 Methodology

(1) Daily NDVI Interpolation Because the MODIS sensor isinfluenced by cloud cover only biweekly MODIS radianceswere used to calculate the NDVI products at different spatialresolutions The daily NDVI varies in a near-sinusoidalfashion through all the days every year To provide NDVIestimates on a daily basis 13 daily NDVI values of eachyear (except 2002) and the NDVI obtaining day of theyears between 2003ndash2011 were fitted by using the sinusoidalmethod as

NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863

are the regression coefficients NDVI119889

is the daily NDVI value and119863 is the day of the year


Table 12 Sources of land surface data used in the downscaling of soil moisture and their spatial resolution and temporal repeat [61]

Source Data Spatial resolution Temporal repeat

NLDAS Soil moisture content (0ndash10 cm layer kgm2) 18 degree (125 km) HourlySurface skin temperature (K) 18 degree (125 km) Hourly

AVHRR Normalized difference vegetation index (NDVI) 5 km Daily

MODIS Normalized difference vegetation index (NDVI) 5 km BiweeklyLand surface temperature (K) 1 km Daily

AMSR-E Soil moisture content (m3m3) 14 degree (25 km) DailyMesonet Surface soil water content (0ndash5 cm layer) 116sim117 stations 5 minutesLittle Washita Watershed Soil moisture measurement (0ndash5 cm layer) 9 stations Hourly

This equation was applied to the 5 km NDVI data forall years to obtain daily 5 km NDVI values Then theinterpolated results were resampled to 125 km to match upwith NLDAS pixels Similarly the daily 1 km NDVI maps forthe three months studied in this paper were generated usingthis method

(2)Thermal Inertia TheoryThe concept of thermal inertia isnamely the resistance of a material to temperature changewhich is indicated by the time-dependent variations intemperature during a full heatingcooling cycle It is definedas the square root of the product of thematerialrsquos bulk thermalconductivity and volumetric heat capacity where the latter isthe product of density and specific heat capacity

119868 = radic119896120588119888(20)

where 119896 is the coefficient of thermal conductivity 120588 is densityand 119888 is the specific heat capacity

An approximation to thermal inertia can be obtainedfrom the amplitude of the diurnal temperature curve Thetemperature of amaterial with low thermal inertiawill changesignificantly during the day while the temperature of amaterial with high thermal inertia will not change as muchThe volumetric heat capacity depends on soil moistureTherehave been many such attempts in the past since HCMM(Heat Capacity Mapping Mission) the first of a series ofApplications ExplorerMissions (AEM) [115]The objective ofthe HCMM was to provide comprehensive accurate high-spatial-resolution thermal surveys of the surface of the earthto determine thermal inertia

Therefore it is our assertion that lower values of dailyaverage soil moisture 120579av will correspond to higher value ofdaily temperature difference Δ119879

119904

and vice versa Δ119879119904

can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)

where 119879max 119879min are the daily highest and lowest tempera-tures respectively The two local overpass times of MODISapproximately correspond to the highest and lowest temper-atures

(3) Construction of the Downscaling Model The MODISsensor provides two very important products NDVI andsurface temperature (119879

119904

) In this study these two variables

were extracted for each 1 km MODIS pixel (119894 119895) in the14∘ gridded AMSR-E radiometer data We denote thesevariables by NDVI (119894 119895) and 119879

119904

(119894 119895) The radiometer-derivedsoil moisture 120579 corresponds to the daytime 1330 overpass120579

119886 and nighttime 130 overpass 120579119901 for the entire 14∘ pixelThe daytime and the nighttime overpass soil moisture valuesfor each of the MODIS pixels are referred as 120579119886(119894 119895) and120579

119901

(119894 119895)The average value of the pixel soil moisture is denotedby 120579av(119894 119895) which refers to the arithmetic mean of the soilmoisture for the 1 km pixel for the morning and nightoverpassThese are depicted in Figure 17TheMODIS sensoron Aqua was used because it matched the time of AMSR-Esoil moisture estimates

There are three theories that motivate the pixel-baseddownscaling algorithm First we must consider that thesoil moisture history of each pixel is unique with regardto precipitation surface overflow and runoff and can besummarized by the average soil moisture 120579av(119894 119895) Also basedon the thermal inertia theory the thermal inertia and soilmoisture dependon soil thermal conductivity which for awetpixel will show a smaller change while a dry pixel will showlarger change in surface temperature [91] Finally vegetationbiomass of each pixel will vary and can modulate the changeof surface temperature which is represented by the dailytemperature difference Δ119879

119904

[49 116] The comparison of thepixel sizes between the three datasets and the look-up curvebuilding method is shown in Figure 17

The key to the proposed disaggregation procedure isestablishing the relationship between the change in surfacetemperature and the average soil moisture for the 1 km pixel

Since the NLDAS-2 data are at 18∘ resolution while theAMSR-E data are at 14∘ resolution 4 NLDAS-2 pixels arewithin each AMSR pixel To construct the look-up curves weused the NLDAS-2 output plotted separately for each month(12 plots for each 14∘ pixel) For each plot we used datafrom all the months of the study period (ie the July plotwill have data for the surface temperature change and theaverage soil moisture for all years from 1979 to present) Thedata for equal NDVI lines at increments of 03 in NDVI wassubsequently organized For example during some months(eg January) the vegetation growth was limited and fewNDVI curves in theUpperMidwest were constructed (maybeone corresponding to NDVI of 0 and another correspondingto NDVI of 03) On the other hand during other periods


Table 13 Comparison statistics between the 1 km downscaled NLDAS and AMSR-E soil moisture compared to the Oklahoma Mesonet for6 relatively clear days RMSE bias and standard deviations are in (m3m3) [61]

Day Dataset 119877

2

RMSE Standard deviation Bias

May 9 20041 km downscaled 0223 0119 0043 minus0112

AMSR-E 0050 0129 0053 minus0114NLDAS 0171 0105 0074 minus0077



July 17 20051 km downscaled 0020 0168 0055 minus0155

AMSR-E lowast 0160 0063 minus0136NLDAS 0005 0099 0059 minus0068



August 9 20051 km downscaled 0010 0167 0050 minus0155




lowast

lt0001

Table 14 Comparison statistics between the 1 km downscaled NLDAS and AMSR-E soil moisture compared to the Little Washita soilmoisture observations for 6 relatively clear days RMSE bias and standard deviations are in (m3m3) [61]

Day Dataset Number of points RMSE Standard deviation Bias

May 4 20041 km downscaled 8 0043 0015 0009

AMSR-E 3 mdash mdash minus0019NLDAS 6 0040 0020 0016


AMSR-E 3 mdash mdash 0005NLDAS 6 0055 0017 0035

July 3 20051 km downscaled 6 0066 0070 0006




August 2 20051 km downscaled 7 0031 0019 0024


August 4 20051 km downscaled 7 0022 lowast 0022


lowast

lt0001

such as July in the Upper Midwest rapid changes in NDVIdue to crop growth could occur and many NDVI curvesranging fromNDVI of 10 to 50 would be createdThe curveswere fitted to these pointsmdash930 points corresponding to theAMSR-E am overpass time (30 years of July data times 31 days)and 930 points corresponding to AMSR-E pm overpass time

A simple linear regression model between the daily aver-age soil moisture 120579av(119894 119895) and daily temperature differenceΔ119879

119904

(119894 119895) for all 30 years for each month was developed asfollows

120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)

119879119861 LV difference (July 7thndashJuly 5th)

44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5

120590 LVV difference

Δ120590

(dB)

Figure 11 Difference images for change in PALS LV brightness temperatures at 400m resolution and change in AIRSAR LVV backscatter at30m resolution for the period July 5 to July 7 (July 5ndashJuly 7)The spatial patterns corresponding to wetting or drying are strikingly consistentin both images indicating that the AIRSAR LVV channel is sensitive to near-surface soil moisture [55]

1

3

5

7

0 10 20minus3

minus1

minus40 minus30 minus20 minus10

119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)

July 5thndashJuly 7th

Figure 12 Chance in PALS L band V pol brightness temperatureplotted versus change in AIRSAR LVV channel backscattering coef-ficients AIRSAR data has been aggregated to the PALS resolution of400m Change is computed for the days July 5 to July 7 [55]

where 119894 119895 represent the pixel location 1198860

and 1198861

are regres-sion model coefficients which correspond to several dif-ferent NDVI intervals The growing season between MayndashSeptember was examined in this study and the NDVI was

subdivided to three intervals 0sim03 03sim06 and 06sim1 Foreach month three NLDAS-based look-up curves of eachpixel which corresponded to the three NDVI intervals werebuilt and the regression coefficients were obtained

(4) Correction of 1 km Downscaled Soil Moisture On a dailybasis we used the curves corresponding to theNLDAS-2 dataclosest to theMODIS pixel to calculate the 1 km 120579av(119894 119895) fromthe Δ119879

119904

(119894 119895) of each 1 km MODIS pixel We then averaged120579

av(119894 119895) from all the 1 km MODIS pixels and compared the

values to daily average AMSR-E soil moisture (Θ119886 + Θ119901)2and then corrected each 120579av(119894 119895) with the difference between(Θ119886+Θ119901)2 and 120579av(119894 119895)The corrected soil moisture 120579avc(119894 119895)is given by

120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)

We subsequently generated daily values of 120579avc(119894 119895) at 1 kmThis satisfies the following conditions (a) the average of thedisaggregated soil moistures over the AMSR-E pixel is thesame as that recorded by AMSR-E (b) the MODIS 1 kmvegetation modulates the distribution of the disaggregatedsoil moisture through its influence in the change in surfacetemperature to morning and evening averaged soil moisturecomputed by NLDAS and (c) the 1 km scale changes insurface temperature reflect on soil moisture distribution asevidenced in the disaggregated soil moisture In addition this


0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01

119898119907 change (July 5ndashJuly 7)

(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)

Figure 13 100m in situ soil moisture change (119909-axis) compared with the 100m resolution estimates of soil moisture change derived fromthe algorithm for the period July 5 to July 7 Plot (a) has all the points with RMSE = 0046 and plot (b) has 7 outliers removed with RMSE =0032 [55]

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03

minus01minus02minus03

119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)

Figure 14 100m in situ soil moisture change (119909-axis) compared with the 100m resolution estimates of soil moisture change derived from thealgorithm for the period July 5 to July 8 Plot (a) has all the points with RMSE = 0046 and plot (b) has the data points from fields WC30 andWC31 removed resulting in an RMSE of 0024 [55]

methodology can only be applied over areas with no cloudcover

424 Results

(1) 120579av minus Δ119879119904

Look-Up Curves Figure 18 shows the regressionfitting results between NLDAS-derived daily temperature

difference and daily average soil moisture of a pixel (lati-tude 101875∘Wsim102∘W longitude 35125∘Nsim35625∘N) forthe three growing months May July and August It can benoticed that the daily average soil moisture values for allthe months are in the range from 005sim03 The points thatcorrespond to each NDVI interval (0ndash03 03ndash06 and 06ndash10) yield nearly parallel lines and the 1198772 value of the fit forJuly are 054 056 and 040 respectively for each NDVI


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S

Mesonet StationsLittle Washita Stations

Figure 15 Imagery maps of study region of Oklahoma and the Little Washita Watershed The locations of the Mesonet Stations are denotedin open yellow circles and the soil moisture sites for Little Washita are noted in red dots [61]

interval Further the daily average soil moisture has a neg-ative relationship with the daily temperature change whichis consistent with the assumptions that (a) the temperaturechange between morning and night is determined by thewetness of pixel and (b) the vegetation modulates the changeof surface temperature and the pixel with higher vegetation isless sensitive to the temperature change

(2) 1 km Downscaled Soil Moisture Analysis Three maps ofdaily 1 km downscaled soil moisture are shown as examplesMay 22 2004 July 17 2005 and August 9 2005 (Figure 19)The 1 km downscaled soil moistures in the lowerMideast partof Oklahoma are missing which may be due to two reasons(a) precipitation and heavy cloud cover often dominatethis area especially in growing season which may resultin missing MODIS surface temperature data and (b) thisarea corresponds to a gap between AMSR-E sensor swathsThese downscaled maps illustrate the pattern of soil moisturedistribution whereby the soil moisture content graduallyincreases from west to east which roughly corresponds tothe NDVI variation in Oklahoma In addition the 1 km

downscaled soil moisture maps also exhibit similar patternsas those of AMSR-E and NLDAS

For each of the days depicted in Figures 20(a)ndash20(c) thecomparison of the 1 kmdownscaled soilmoisture is shown onthe top panel the AMSR-E 14∘ soil moisture in the centralpanel and the NLDAS 18∘ soil moisture in the bottom panelThe NLDAS soil moisture always has complete coveragebecause it is not impacted by cloud cover or missing data dueto swaths not overlapping with each other On May 22 2004a wet area existed in the northeast corner of Oklahoma andthis was not captured by the AMSR-E or the 1 km downscaledsoil moisture Even so in general the spatial patterns of thethree estimates resemble each other for the May 22 2004case On July 17 2005 the western half of Oklahoma was verydry with soil moisture close to 002 with larger values in theeast The spatial structure shown by the 1 km soil moistureshows variability even in the dry western part of the statewhich cannot be observed using the 14∘ AMSR-E estimatesalone In addition the 1 km soilmoisture captures thewet areain the east central part of the state A similar west-to-east dry-


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(c) AMSR-E 120579av from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(d) NLDAS 120579av from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1

(e) NDVI from July 21 2005

Figure 16 Maps of Variables used in the soil moisture downscaling algorithm from July 21 2005 over Oklahoma (a) MODIS Aqua 1 kmland surface temperature during the day (b) MODIS Aqua 1 km land surface temperature at night (c) 14∘ spatial resolution AMSR-E soilmoisture (d) 18∘ spatial resolution NLDAS soil moisture (e) MODIS Aqua 1 km NDVI [61]

AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)

to constant NDVICurves corresponding

Δ119879119904(119894119895)

120579av(119894119895)

(b)

Figure 17 (a) Shows the various elements in the disaggregation procedure and (b) shows construction of the curves corresponding to constantNDVI between average soil moisture and change in surface temperature [61]

to-wet pattern was observed in all the estimates of the soilmoisture for August 9 2005

(3) Validation by Oklahoma Mesonet Soil Moisture DataTable 13 shows that the 1198772 values of the 1 km downscaled soil

moistures are better than AMSR-E and NLDAS which rangefrom 001sim036 RMSE (range from 0119sim0168m3m3)standard deviation (range from 0043sim0058m3m3) andbias (ranges from minus0158simminus0112m3m3) The 1198772 values forNLDAS ranges from 0002 to 0264 and those for AMSR-E


00

5 10 15 20 25 30 35 40Daily temperature difference (K)

005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03 03 lt NDVI lt 0606 lt NDVI lt 1

May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August

03 lt NDVI lt 0606 lt NDVI lt 1

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July


Figure 18 Daily temperature difference versus daily average soil moisture corresponding to latitude 101875∘Wsim102∘W longitude 35125∘Nsim35625∘N and different NDVI values for May June and July [61]

fromlt0001 to 0387These values are considerably lower thanthose corresponding to the 1 km downscaled soil moisturesBesides the RMSE values for NLDAS and AMSR-E rangefrom 0099sim0108m3m3 and 0114sim0160m3m3 respec-tively which are similar to the range of 1 km downscaledsoil moistures Comparing the correlation scatter plots ofthe three datasets versus the Mesonet data (Figures 20(a)ndash20(c)) it can be seen that the 1 km downscaled soil moisturehas fewer points than AMSR-E and NLDAS The reason forthis is due to cloudiness and the lack of availability of thesurface temperature Thus it was not possible to downscalethe AMSR-E soil moisture to produce the 1 km soil moisture

It should be noted that the soil moisture values of allthree datasets are biased which indicate that the simulated

soil moisture values are lower than the in situ Mesonetobservation values This could be attributed to the following(a) The accuracy of AMSR-E soil moisture is limited andapproximately 010This methodology is based on preservingthe 25 km mean soil moisture same as the AMSR-E soilmoisture estimates So any overall day-to-day bias presentin the AMSR-E soil moisture retrievals will be present inthe disaggregated 1 km estimates (b) The MODIS-retrieveddaytime surface temperature is higher than the NLDAS-2land surface model output particularly during the growingseason which may be the cause of the daily temperaturedifference being greater than NLDAS and consequentlythe downscaled soil moisture being lower than NLDAS(c) The Oklahoma Mesonet consists of Campbell Scientific


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) NLDAS 120579 from August 9 2005

(iii) 1 km120579 from August 9 2005

(ii) AMSR-E 120579avav

av

from August 9 2005

(b)

Figure 19 (a) Maps of the NLDAS AMSR-E and 1 km soil moisture (m3m3) from May 22 2004 ((i)ndash(iii)) and July 17 2005 ((iv)ndash(vi)) (b)maps of the NLDAS AMSR-E and 1 km soil moisture (m3m3) from August 9 2005 [61]


229-L sensors which are soil matric potential sensors (thenconverted to volumetric soil moisture) which may havebiases when compared to the gravimetric and neutron probesamples of which RMSE is between 0006sim0052 [45]

(4) Validation Using Little Washita Watershed Soil MoistureData Table 14 shows the statistical results for six days ofLittle Washita Watershed soil moisture comparisons with1 km downscaled AMSR-E and NLDAS AMSR-E statisticsare not shown because these were less than three points ofAMSR-E soil moisture over this period Since the compar-isons require high coverage of valid 1 km downscaled soilmoisture values within the Little Washita region the daysused for the Little Washita comparisons are different fromthose used for theMesonet comparisons Two clear days fromeach month (May 4 2004 May 6 2004 July 3 2005 July 172005 August 2 2005 and August 4 2005) were selected Inaddition the correlation plots including four clear days andthe total 15 clear days of soil moisture in May in the LittleWashita region versus the 1 km AMSR-E and NLDAS soilmoisture are presented in Figures 21 and 22

For the 1 km downscaled soil moisture the RMSE valuesrange from 0022sim0077 while the standard deviations rangefrom lt0001sim007 and bias ranges from minus0047sim0032 Thesestatistical results demonstrate that the 1 km downscaled soilmoisture values are equivalent to the NLDAS and better thanthe accuracy of AMSR-E soil moisture values In additionthe downscaled soil moisture values have a higher spatialresolution than the NLDAS soil moisture values since theyare at 1 km as opposed to 125 km for NLDAS This willbe particularly important in small watershed studies whenone pixel of NLDAS might cover the whole catchment andnot provide information on spatial variability Moreover theresults also indicate that the 1 km downscaled soil moisturehas a better agreement with Little Washita than Mesonetalthough the variables of July 17 2005 do not perform as wellas the other days

By analyzing the single days correlation plots for May(Figures 21ndash22) it can be seen that the 1 km downscaled soilmoistures match up well with the AMSR-E and NLDAS soilmoisturesThe correlation for theMay 2004 1 km downscaledresults versus the Little Washita soil moisture was 1198772 = 04with an RMSE of 0018 The statistical results are also betterthan the comparison with Mesonet soil moistures A smallcatchment such as the Little Washita might be covered byonly a single pixel of AMSR-E pixel or a few NLDAS pixelsthe downscaled soil moisture offers the distinct advantage ofhaving many more pixels (900 1 km pixels versus 6 NLDASpixels for Little Washita Watershed) and provides spatialvariability information

The frequency distribution of the differences betweenLittle Washita soil moisture values versus 1 km downscaledAMSR-E and NLDAS soil moisture values are presentedin Figures 23(a)ndash23(c) respectively For the Little Washitasoil moisture values within a particular AMSR-E or NLDASare averaged their correlation plots have fewer points thanthe 1 km downscaled soil moisture values The results indi-cate that the differences between the 1 km downscaled soil

moistures and Little Washita results generally distributerange from minus005ndash01 and the differences of downscaled soilmoisture is around 7ndash36 of the total which is similar tothe NLDAS

5 Conclusions and Discussions

Since this paper has discussed three major studies theconclusions from each of them will be highlighted separatelyin the subsections below In Section 51 the results from thefield experiment study of SMEX02 conducted in Ames Iowain summer 2002 will be discussed The major findings fromthe study on disaggregation of passive microwave data usingactive radar observations will be dealt with in Sections 52and 53 will explain the major findings from the use of visiblenear infrared data in disaggregation of AMSR-E data overOklahoma

51 Use of PALS in the SMEX02 Field Experiment Thissection applied existing algorithms to previously untestedconditions of vegetation cover The sensitivity of PALSradiometer and radar to soil moisture in these conditions hasbeen studied Soil moisture retrievals were performed usingstatistical as well as physical modeling techniques The rootmean square error associated with soil moisture retrieval bystatistical regression was around 0051 gg while soil moistureretrieval using the zero order incoherent radiative transfermodel gave retrieval errors of around 0036 gg gravimetricsoil moisture Lower retrieval error associated with usingmultiple channels as compared to a single channel in soilmoisture retrieval by statistical regression has been demon-strated Existing algorithms for passive radiative transfer wereshown to perform satisfactorily even though the vegetationcover was considerable Vegetation plays a role in reducingthe sensitivity of the PALS radar and radiometer and therebyincreasing soil moisture retrieval error has been analyzed

We observed good agreement between the radar- andradiometer-predicted soil moisture The retrieved values ofsoil moisture tend to be overestimated which demonstratesthe limitations of the change detection algorithm employedin this section wherein the assumption that vegetation androughness effects are present only as a bias in PALS active andpassive observations are shown to be sources of error

52 Use of Radar for Disaggregation of Passive Microwave SoilMoisture In this section a simple algorithm for estimation ofchange in soil moisture at the spatial resolution of radar usinglow-resolution estimate of soil moisture from radiometer andcopolarized backscattering coefficients has been proposedThe subpixel scale surface roughness variability does not playan important role in radar sensitivity to soil moisture atthe L-band Observations from combined radarradiometerdata from SMEX02 results from previous studies and IEMsimulations have been presented in support of this argumentRadar sensitivity to soil moisture at the L-band has beenassumed to be a function of vegetation opacity only andfurther a simple soil moisture change estimation algorithmhas been developed Application of the algorithm to data


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05

Mesonet soil moisture (m3m3) Mesonet soil moisture (m3m3)

Mesonet soil moisture (m m33 )Mesonet soil moisture (m3m3)

May 22 2004 (Oklahoma) May 22 2004 (Oklahoma)


1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108

1 km downscaled AMSR-ENLDAS

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05


Mesonet soil moisture (m3m3)Mesonet soil moisture (m3m3)

July 17 2005 (Oklahoma) July 17 2005 (Oklahoma)


1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05



August 9 2005 (Oklahoma) August 9 2005 (Oklahoma)


1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)

Figure 20 ((a)ndash(c)) Scatter plots of the 1 km AMSR-E and NLDAS soil moisture versus the Oklahoma Mesonet soil moisture observationsfor May 22 2004 July 17 2005 and August 9 2005 [61]

obtained from the SMEX02 experiments results providedgood results with rootmean square error of prediction of 003and 002 (error for estimated versusmeasured volumetric soilmoisture both at 100m resolution) for two periodsmdashJuly 5 toJuly 7 and July 7 to July 8The1198772 value for both cases was 085

The originality of the approach presented here lies inusing radiometer to estimate soil moisture change at a lowerspatial resolution (but with lower ancillary data requirementsas compared to radar estimation of soil moisture) and thenusing change in radar backscatter to estimate the changein soil moisture at higher spatial resolution The estimatedchange in soil moisture is a hydrologic variable of significantinterest A simple calibration methodology based on leasterror between modeled and measured soil moisture changevalues will allow a better estimation of parameters such as thehydraulic conductivity of soil layers Mattikalli et al reportedthat the 2-day soil moisture change was closely related to thesaturated hydraulic conductivity (119870sat) profile [71] It will bepossible to relate 119870sat from the radarradiometer-algorithm-derived change in soil moisture with the added advantageof higher spatial resolution that will lead to more accurateestimation of water and energy fluxes Future studies on thedirection of radarradiometer combination will have to aimat a better parameterization of the sensitivity relationship

with vegetation opacity allowing the effect of vegetationheterogeneity to be addressed through the 119891(120591) parameterin the algorithm Du et al 2000 [117] have explored thebehavior of soybean and grass canopies over medium roughsurfaces Their results indicate that it should be possible todevelop simple parametric relationships between vegetationopacity and relative sensitivity Estimation of vegetationopacity has been done in the past using optical sensors byestimation of vegetation water content using a proxy suchas NDWI [83] and then using the empirical parameter 119887 torelate vegetation opacity and water content [118] This simpleparameterization however has been shown to be too simplefor canopies such as corn that are electrically thick scatterersand further research is required to find relationships betweenvegetation parameters and relative sensitivity over canopiessuch as corn The approach presented in the paper should beapplicable to data from theHydrosmission at least over areasof low vegetation water content variability The algorithmwill have to be modified to account for vegetation variabilitywithin the radiometer footprint using the 119891(120591) parameterbefore it can be made operational

53 Use of Near Infrared and Visible Data for Disaggrega-tion of AMSR-E Soil Moisture A soil moisture downscaling


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05

Little Washita soil moisture (m3m3)

Estim

ated

soil

moi

sture

(m3m3)

May 4 2004 (Little Washita)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)

July 3 2005 (Little Washita)



0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)

August 2 2005 (Little Washita)

Figure 21 Scatter plot of the 1 km AMSR-E and NLDAS soil moisture versus the Oklahoma Mesonet soil moisture observations for May 42004 May 6 2004 July 3 2005 and August 2 2005 [61]

algorithm based on NLDAS-derived look-up curves thatrelated daily surface temperature change and average dailysoil moisture was developed This algorithm was appliedusing MODIS products of clear days during crop growingseasons (May July and August of 2004sim2005) in OklahomaTwo sets of validation data namely Oklahoma Mesonet andLittle Washita soil moisture observations have been usedto compare with the three estimates 1 km downscaled soilmoisture values AMSR-E soil moisture values and NLDASsoil moisture values Statistical analyses and plots were usedfor analyzing accuracy of the downscaling algorithm

Our results indicate the following (a)The look-up regres-sion curves support our assumption that the surface temper-ature change depends on the wetness of the land surface andthat the vegetation modulates this relationship (b) The 1 km

downscaled maps provide details on the soil moisture spatialdistribution patterns in Oklahoma as opposed to the AMSR-EmapsThey also compare well with OklahomaMesonet soilmoisture values (c)The comparisons of all three datasetswiththe Oklahoma Mesonet soil moisture observations show an119877

2 for the 1 km downscaled soil moisture ranging from 001sim036 RMSE values ranging from 0119sim0168m3m3 andstandard deviation ranging from 0043sim0058m3m3 Thestatistical comparisons show that the 1 km downscaled resultscorrelate better to the Oklahoma Mesonet soil moistureobservations thandoAMSR-E andNLDAS soilmoistures (d)The statistical results for the 1 km downscaled soil moisturecomparing with Little WashitaWatershed soil moisture showgood accuracy for the downscaling algorithm Single-daycomparisons showed RMSE values from 0022sim0077m3m3


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


May 2004 (Little Washita)1 k

m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


May 2004 (Little Washita)

AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


Figure 22 Scatter plot comparing AMSR-E NLDAS and 1 km downscaled soil moisture to the Little Washita soil moisture observations forall days of May 2004 [61]

standard deviations fromlt0001sim007m3m3 and bias valuesfrom minus0047sim0032m3m3 Taken as a whole for all of theclear days in May 2004 the 1198772 and RMSE are 04 and0018m3m3 respectively The errors between estimated andground data used for validation for 1 km downscaled dataare generally from minus007sim01m3m3 so the downscaled soilmoisture has an error of 7sim36 of the total

However there are still several limitations that exist in thisalgorithm (a) the MODIS temperature and NDVI productsare often influenced by the cloud coverage therefore thismethod is not an all-weather algorithm for downscaling (b)the accuracy of the AMSR-E and NLDAS soil moisture deter-mines the accuracy of the 1 km downscaled soil moisture and(c) Only vegetation and temperature were used in developingthis downscaling algorithm and the high spatial resolutiondata of these variables would be required This methodology

is based on preserving the 25 km mean soil moisture sameas the AMSR-E soil moisture estimates So any overall day-to-day bias present in the AMSR-E soil moisture retrievalswill be present in the disaggregated 1 km estimates But if wecompare our results with those reported in the literature andpresented in Section 1 and Table 1 we note that this analysisincluded a large area (the entire state of Oklahoma) anda moderate period of time as opposed to some previousstudies which only included shorter-term field experimentsor smaller catchments However our correlations values for119877

2 do comparewell with the values noted in these studies [50]of 014ndash021 the RMSE shows similar favorable comparisons

Future work that will combine this approach with ourprevious active-passive downscaling approach [55] is beingpursued and this will offermuch better progress in downscal-ing of soil moisture for catchment studies


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (1 km downscaled)

minus015 minus01 minus0050

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)


Figure 23 Frequency distribution of difference between the 1 km AMSR-E and NLDAS estimates of soil moisture and the Little Washitasoil moisture observations for May 2004 [61]

References

[1] K L Brubaker and D Entekhabi ldquoAnalysis of feedbackmechanisms in land-atmosphere interactionrdquo Water ResourcesResearch vol 32 no 5 pp 1343ndash1357 1996

[2] T Delworth and S Manabe ldquoThe influence of soil wetness onnear-surface atmospheric variabilityrdquo Journal of Climate vol 2pp 1447ndash1462 1989

[3] R A Pielke ldquoInfluence of the spatial distribution of vegetationand soils on the prediction of cumulus convective rainfallrdquoReviews of Geophysics vol 39 no 2 pp 151ndash177 2001

[4] J Shukla and Y Mintz ldquoInfluence of land-surface evapotran-spiration on the earthrsquos climaterdquo Science vol 215 no 4539 pp1498ndash1501 1982

[5] Jy-Tai Chang and P J Wetzel ldquoEffects of spatial variations ofsoil moisture and vegetation on the evolution of a prestormenvironment a numerical case studyrdquoMonthlyWeather Reviewvol 119 no 6 pp 1368ndash1390 1991

[6] E Engman ldquoSoil moisture the hydrologic interface betweensurface and ground watersrdquo in Remote Sensing and Geo-graphic Information Systems for Design and Operation of Water

Resources Systems International Association of HydrologicalSciences no 242 1997

[7] J Mahfouf E Richard and P Mascarat ldquoThe influence of soiland vegetation on Mesoscale circulationsrdquo Journal of Climateand Applied Climatology vol 26 pp 1483ndash1495 1987

[8] J Laccini T Carlson and T Warner ldquoSensitivity of the GreatPlains severe storm environment to soil moisture distributionrdquoMonthly Weather Review vol 115 pp 2660ndash2673 1987

[9] D Zhang and R A Anthes ldquoA high-resolution model of theplanetary boundary layermdashsensitivity tests and comparisonswith SESAME-79 datardquo Journal of Applied Meteorology vol 21no 11 pp 1594ndash1609 1982

[10] P R Houser W J Shuttleworth J S Famiglietti H V GuptaK H Syed and D C Goodrich ldquoIntegration of soil moistureremote sensing and hydrologic modeling using data assimila-tionrdquo Water Resources Research vol 34 no 12 pp 3405ndash34201998

[11] S ZhangH LiW Zhang CQiu andX Li ldquoEstimating the soilmoisture profile by assimilating near-surface observations withthe ensemble Kalman Filter (EnKF)rdquo Advances in AtmosphericSciences vol 22 no 6 pp 936ndash945 2005


[12] W Ni-Meister P R Houser and J P Walker ldquoSoil moistureinitialization for climate prediction assimilation of scanningmultifrequency microwave radiometer soil moisture data intoa land surface modelrdquo Journal of Geophysical Research D vol111 no 20 Article ID D20102 2006

[13] J P Hollinger J L Pierce and G A Poe ldquoSSMI instrumentevaluationrdquo IEEE Transactions on Geoscience and Remote Sens-ing vol 28 no 5 pp 781ndash790 1990

[14] E Njoku B Rague and K Fleming The Nimbus-7 SMMRPathfinder Brightness Data Set Jet Propulsion Lab PasadenaCalif USA 1998

[15] S Paloscia G Macelloni E Santi and T Koike ldquoA multifre-quency algorithm for the retrieval of soil moisture on a largescale using microwave data from SMMR and SSMI satellitesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 39no 8 pp 1655ndash1661 2001

[16] A Guha and V Lakshmi ldquoSensitivity spatial heterogeneityand scaling of C-band microwave brightness temperatures forland hydrology studiesrdquo IEEE Transactions on Geoscience andRemote Sensing vol 40 no 12 pp 2626ndash2635 2002

[17] A Guha and V Lakshmi ldquoUse of the Scanning MultichannelMicrowave Radiometer (SMMR) to retrieve soil moisture andsurface temperature over the central United Statesrdquo IEEETransactions on Geoscience and Remote Sensing vol 42 no 7pp 1482ndash1494 2004

[18] J Wen T J Jackson R Bindlish A Y Hsu and Z BSu ldquoRetrieval of soil moisture and vegetation water contentusing SSMI data over a corn and soybean regionrdquo Journal ofHydrometeorology vol 6 no 6 pp 854ndash863 2005

[19] V Lakshmi ldquoSpecial sensor microwave imager data in fieldexperiments FIFE-1987rdquo International Journal of Remote Sens-ing vol 19 no 3 pp 481ndash505 1998

[20] V Lakshmi E F Wood and B J Choudhury ldquoA soil-canopy-atmosphere model for use in satellite microwave remote sens-ingrdquo Journal of Geophysical Research D vol 102 no 6 pp 6911ndash6927 1997

[21] V Lakshmi E F Wood and B J Choudhury ldquoInvestigationof effect of heterogeneities in vegetation and rainfall on simu-lated SSMI brightness temperaturesrdquo International Journal ofRemote Sensing vol 18 no 13 pp 2763ndash2784 1997

[22] V Lakshmi E F Wood and B J Choudhury ldquoEvaluationof Special Sensor MicrowaveImager satellite data for regionalsoil moisture estimation over the Red River basinrdquo Journal ofApplied Meteorology vol 36 no 10 pp 1309ndash1328 1997

[23] L Li P Gaiser T J Jackson R Bindlish and J Du ldquoWindSatsoil moisture algorithm and validationrdquo in Proceedings of theInternational Geoscience and Remote Sensing Symposium pp1188ndash1191 Barcelona Spain July 2007

[24] H Gao E F Wood T J Jackson M Drusch and R BindlishldquoUsing TRMMTMI to retrieve surface soil moisture overthe southern United States from 1998 to 2002rdquo Journal ofHydrometeorology vol 7 no 1 pp 23ndash38 2006

[25] M Owe R de Jeu and T Holmes ldquoMultisensor historicalclimatology of satellite-derived global land surface moisturerdquoJournal of Geophysical Research F vol 113 no 1 Article IDF01002 2008

[26] W Wagner V Naeimi K Scipal R Jeu and J Martınez-Fernandez ldquoSoil moisture from operational meteorologicalsatellitesrdquo Hydrogeology Journal vol 15 no 1 pp 121ndash131 2007

[27] C Rudiger J C Calvet C Gruhier T R H Holmes R A Mde Jeu and W Wagner ldquoAn intercomparison of ERS-Scat andAMSR-E soil moisture observations with model simulations

over Francerdquo Journal of Hydrometeorology vol 10 no 2 pp 431ndash447 2009

[28] C S Draper J P Walker P J Steinle R A M de Jeu and TR H Holmes ldquoAn evaluation of AMSR-E derived soil moistureover Australiardquo Remote Sensing of Environment vol 113 no 4pp 703ndash710 2009

[29] R A M Jeu W Wagner T R H Holmes A J Dolman N CGiesen and J Friesen ldquoGlobal soil moisture patterns observedby space borne microwave radiometers and scatterometersrdquoSurveys in Geophysics vol 29 no 4-5 pp 399ndash420 2008

[30] K Imaoka M Kachi H Fujii et al ldquoGlobal change observationmission (GCOM) for monitoring carbon water cycles andclimate changerdquo Proceedings of the IEEE vol 98 no 5 pp 717ndash734 2010

[31] E G Njoku and D Entekhabi ldquoPassive microwave remotesensing of soil moisturerdquo Journal of Hydrology vol 184 no 1-2 pp 101ndash129 1996

[32] F T Ulaby P C Dubois and J Van Zyl ldquoRadar mapping ofsurface soil moisturerdquo Journal of Hydrology vol 184 no 1-2 pp57ndash84 1996

[33] T J Schmugge W P Kustas J C Ritchie T J Jackson andA Rango ldquoRemote sensing in hydrologyrdquo Advances in WaterResources vol 25 no 8-12 pp 1367ndash1385 2002

[34] F T Ulaby M Razani and M C Dobson ldquoEffect of vegetationcover on the microwave radiometric sensitivity to soil mois-turerdquo IEEE Transactions on Geoscience and Remote Sensing vol21 no 1 pp 51ndash61 1983

[35] B J Choudhury T J Schmugge R W Newton and A ChangldquoEffect of surface roughness on the microwave emission fromsoilsrdquo Journal of Geophysical Research vol 89 no 9 pp 5699ndash5706 1979

[36] F Ulaby R K Moore and A K Fung Microwave RemoteSensing Active and Passive Vol IIImdashVolume Scattering andEmission Theory Advanced Systems and Applications ArtechHouse Dedham Mass USA 1986

[37] J R Wang J C Shiue T J Schmugge and E T Engman ldquoTheL-band PBMRmeasurements of surface soil moisture in FIFErdquoIEEE Transactions on Geoscience and Remote Sensing vol 28no 5 pp 906ndash914 1990

[38] T Schmugge T J Jackson W P Kustas and J R WangldquoPassive microwave remote sensing of soil moisture resultsfrom HAPEX FIFE and MONSOONrsquo 90rdquo ISPRS Journal ofPhotogrammetry and Remote Sensing vol 47 no 2-3 pp 127ndash143 1992

[39] P E OrsquoNeill N S Chauhan and T J Jackson ldquoUse of active andpassive microwave remote sensing for soil moisture estimationthrough cornrdquo International Journal of Remote Sensing vol 17no 10 pp 1851ndash1865 1996

[40] J D Bolten V Lakshmi and E G Njoku ldquoA passive-activeretrieval of soil moisture for the Southern great plains 1999experimentrdquo IEEE Transactions on Geoscience and RemoteSensing vol 41 no 12 pp 2792ndash2801 2003

[41] U Narayan V Lakshmi and E G Njoku ldquoRetrieval of soilmoisture from passive and active LS band sensor (PALS)observations during the Soil Moisture Experiment in 2002(SMEX02)rdquo Remote Sensing of Environment vol 92 no 4 pp483ndash496 2004

[42] E G Njoku W J Wilson S H Yueh et al ldquoObservationsof soil moisture using a passive and active low-frequencymicrowave airborne sensor during SGP99rdquo IEEE Transactionson Geoscience and Remote Sensing vol 40 no 12 pp 2659ndash2673 2002


[43] F Brock K Crawford R L Elliott et al ldquoThe oklahomamesonetmdasha technical overviewrdquo Journal of Atmospheric andOceanic Technology vol 12 no 1 pp 5ndash19 1995

[44] B G Illston J B Basara and K C Crawford ldquoSeasonal tointerannual variations of soil moisturemeasured inOklahomardquoInternational Journal of Climatology vol 24 no 15 pp 1883ndash1896 2004

[45] B G Illston J B Basara D K Fisher et al ldquoMesoscalemonitoring of soil moisture across a statewide networkrdquo Journalof Atmospheric and Oceanic Technology vol 25 no 2 pp 167ndash182 2008

[46] S E Hollinger and S A Isard ldquoA soil moisture climatology ofIllinoisrdquo Journal of Climate vol 7 no 5 pp 822ndash833 1994

[47] G L SchaeferMHCosh andT J Jackson ldquoTheUSDAnaturalresources conservation service soil climate analysis network(SCAN)rdquo Journal of Atmospheric and Oceanic Technology vol24 no 12 pp 2073ndash2077 2007

[48] G C HeathmanMH Cosh E Han T J Jackson L GMcKeeand S McAfee ldquoField scale spatiotemporal analysis of surfacesoil moisture for evaluating point-scale in situ networksrdquoGeoderma vol 170 pp 195ndash205 2012

[49] O Merlin A Al Bitar J P Walker and Y Kerr ldquoAn improvedalgorithm for disaggregating microwave-derived soil moisturebased on red near-infrared and thermal-infrared datardquo RemoteSensing of Environment vol 114 no 10 pp 2305ndash2316 2010

[50] M Piles A CampsMVall-llossera et al ldquoDownscaling SMOS-derived soil moisture usingMODIS visibleinfrared datardquo IEEETransactions on Geoscience and Remote Sensing vol 49 no 9pp 3156ndash3166 2011

[51] O Merlin A Chehbouni J P Walker R Panciera and Y HKerr ldquoA simple method to disaggregate passive microwave-based soil moisturerdquo IEEE Transactions on Geoscience andRemote Sensing vol 46 no 3 pp 786ndash796 2008

[52] O Merlin A Al Bitar J P Walker and Y Kerr ldquoA sequentialmodel for disaggregating near-surface soil moisture observa-tions using multi-resolution thermal sensorsrdquo Remote Sensingof Environment vol 113 no 10 pp 2275ndash2284 2009

[53] D Entekhabi E G Njoku P E OrsquoNeill et al ldquoThe soil moistureactive passive (SMAP)missionrdquo Proceedings of the IEEE vol 98no 5 pp 704ndash716 2010

[54] T Schmugge P Gloersen T Wilheit and F Geiger ldquoRemotesensing of soil moisture with microwave radiometersrdquo Journalof Geophysical Research vol 79 no 2 pp 317ndash323 1974

[55] U Narayan V Lakshmi and T J Jackson ldquoHigh-resolutionchange estimation of soil moisture using L-band radiometerand radar observationsmade during the SMEX02 experimentsrdquoIEEE Transactions on Geoscience and Remote Sensing vol 44no 6 pp 1545ndash1554 2006

[56] UNarayan andV Lakshmi ldquoCharacterizing subpixel variabilityof low resolution radiometer derived soil moisture using highresolution radar datardquoWater Resources Research vol 44 no 6Article IDW06425 2008

[57] N N Das D Entekhabi and E G Njoku ldquoAn algorithm formerging SMAP radiometer and radar data for high-resolutionsoil-moisture retrievalrdquo IEEE Transactions on Geoscience andRemote Sensing vol 49 no 5 pp 1504ndash1512 2011

[58] O Merlin A Al Bitar V Rivalland P Beziat E Ceschia andG Dedieu ldquoAn analytical model of evaporation efficiency forunsaturated soil surfaces with an arbitrary thicknessrdquo Journalof Applied Meteorology and Climatology vol 50 no 2 pp 457ndash471 2011

[59] O Merlin F Jacob J-P Wigneron et al ldquoMultidimensionaldisaggregation of land surface temperature using high-resolution red near-infrared shortwave-infrared andmicrowave-L bandsrdquo IEEE Transactions on Geoscience andRemote Sensing vol 50 no 5 pp 1864ndash1880 2012

[60] O Merlin C Rudiger A Al Bitar et al ldquoDisaggregation ofSMOS soil moisture in Southeastern Australiardquo IEEE Transac-tions on Geoscience and Remote Sensing vol 50 no 5 pp 1556ndash1571 2012

[61] B Fang V Lakshmi R Bindlish T Jackson M Cosh and JBasara ldquoPassive microwave soil moisture downscaling usingvegetation and surface temperaturerdquo Vadose Zone Journal Inreview

[62] M A Friedl D K McIver J C F Hodges et al ldquoGlobal landcover mapping from MODIS algorithms and early resultsrdquoRemote Sensing of Environment vol 83 no 1-2 pp 287ndash3022002

[63] J R Wang and T J Schmugge ldquoAn empirical model for thecomplex dielectric permittivity of soils as a function of watercontentrdquo IEEE Transactions on Geoscience and Remote Sensingvol GE-18 no 4 pp 288ndash295 1980

[64] M C Dobson F T Ulaby M T Hallikainen and M AEl-Rayes ldquoMicrowave dielectric behavior of wet soilmdashpart 2dielectric mixingmodelsrdquo IEEE Transactions on Geoscience andRemote Sensing vol GE-23 no 1 pp 35ndash46 1985

[65] A K Fung Z Li and K S Chen ldquoBackscattering froma randomly rough dielectric surfacerdquo IEEE Transactions onGeoscience and Remote Sensing vol 30 no 2 pp 356ndash369 1992

[66] T Schmugge ldquoRemote sensing of soil moisturerdquo inHydrologicalForecasting M G Anderson and T P Burt Eds John Wiley ampSons New York NY USA 1985

[67] R R Gillies and T N Carlson ldquoThermal remote sensingof surface soil water content with partial vegetation coverfor incorporation into climate modelsrdquo Journal of AppliedMeteorology vol 34 no 4 pp 745ndash756 1995

[68] S J Goetz ldquoMulti-sensor analysis ofNDVI surface temperatureand biophysical variables at a mixed grassland siterdquo Interna-tional Journal of Remote Sensing vol 18 no 1 pp 71ndash94 1997

[69] T Mo B J Choudhury T J Schmugge J R Wang and T JJackson ldquoA model for the microwave emission from vegetationcovered fieldsrdquo Journal of Geophysical Research vol 87 pp 11229ndash11 237 1982

[70] E T Engman and N Chauhan ldquoStatus of microwave soilmoisture measurements with remote sensingrdquo Remote Sensingof Environment vol 51 no 1 pp 189ndash198 1995

[71] N M Mattikalli E T Engman L R Ahuja and T J JacksonldquoMicrowave remote sensing of soil moisture for estimation ofprofile soil propertyrdquo International Journal of Remote Sensingvol 19 no 9 pp 1751ndash1767 1998

[72] C A Laymon W L Crosson V V Soman et al ldquoHuntsvillersquo98 an expirement in ground-based microwave remote sensingof soil moisturerdquo International Journal of Remote Sensing vol20 no 2ndash4 pp 823ndash828 1999

[73] E G Njoku and L Li ldquoRetrieval of land surface parametersusing passive microwave measurements at 6-18 GHzrdquo IEEETransactions on Geoscience and Remote Sensing vol 37 no 1pp 79ndash93 1999

[74] Y H Kerr and E G Njoku ldquoSemiempirical model for inter-preting microwave emission from semiarid land surfaces asseen from spacerdquo IEEE Transactions on Geoscience and RemoteSensing vol 28 no 3 pp 384ndash393 1990


[75] YOh K Sarabandi and F TUlaby ldquoAn empiricalmodel and aninversion technique for radar scattering frombare soil surfacesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 30no 2 pp 370ndash381 1992

[76] P C Dubois J van Zyl and T Engman ldquoMeasuring soilmoisture with imaging radarsrdquo IEEETransactions onGeoscienceand Remote Sensing vol 33 no 4 pp 915ndash926 1995

[77] J Shi J Wang A Y Hsu P E OrsquoNeill and E T EngmanldquoEstimation of bare surface soil moisture and surface roughnessparameter using L-band SAR image datardquo IEEE Transactions onGeoscience and Remote Sensing vol 35 no 5 pp 1254ndash12661997

[78] T J Jackson J Schmugge and E T Engman ldquoRemote sensingapplications to hydrology soil moisturerdquo Hydrological SciencesJournal vol 41 no 4 pp 517ndash530 1996

[79] M Owe A A Van De Griend R De Jeu J J De Vries ESeyhan and E T Engman ldquoEstimating soil moisture fromsatellite microwave observations past and ongoing projectsand relevance to GCIPrdquo Journal of Geophysical Research D vol104 no 16 pp 19735ndash19742 1999

[80] J D Villasenor D R Fatland and L D Hinzman ldquoChangedetection on Alaskarsquos North Slope using repeat-pass ERS-1 SARimagesrdquo IEEE Transactions on Geoscience and Remote Sensingvol 31 no 1 pp 227ndash236 1993

[81] M C Dobson and F T Ulaby ldquoActive microwave soil-moistureresearchrdquo IEEE Transactions on Geoscience and Remote Sensingvol 24 no 1 pp 23ndash36 1986

[82] M C Dobson and F T Ulaby ldquoPreliminary evaluation of theSir-B response to soil-moisture surface-roughness and cropcanopy coverrdquo IEEE Transactions on Geoscience and RemoteSensing vol 24 no 4 pp 517ndash526 1986

[83] T J Jackson D Chen M Cosh et al ldquoVegetation water contentmapping using Landsat data derived normalized differencewater index for corn and soybeansrdquo Remote Sensing of Environ-ment vol 92 no 4 pp 475ndash482 2004

[84] M H Cosh T J Jackson R Bindlish and J H PruegerldquoWatershed scale temporal and spatial stability of soil moistureand its role in validating satellite estimatesrdquo Remote Sensing ofEnvironment vol 92 no 4 pp 427ndash435 2004

[85] A S Limaye W L Crosson C A Laymon and E GNjoku ldquoLand cover-based optimal deconvolution of PALS L-band microwave brightness temperaturesrdquo Remote Sensing ofEnvironment vol 92 no 4 pp 497ndash506 2004

[86] L Yunling K Yunjin and J van Zyl ldquoThe NASAJPL air-borne synthetic aperture radar systemrdquo 2002 httpairsarjplnasagovdocumentsgenairsarairsar paper1pdf

[87] K Mallick B K Bhattacharya and N K Patel ldquoEstimatingvolumetric surface moisture content for cropped soils using asoil wetness index based on surface temperature and NDVIrdquoAgricultural and Forest Meteorology vol 149 no 8 pp 1327ndash1342 2009

[88] I Sandholt K Rasmussen and J Andersen ldquoA simple inter-pretation of the surface temperaturevegetation index spacefor assessment of surface moisture statusrdquo Remote Sensing ofEnvironment vol 79 no 2-3 pp 213ndash224 2002

[89] T N Carlson R R Gillies and T J Schmugge ldquoAn interpre-tation of methodologies for indirect measurement of soil watercontentrdquo Agricultural and Forest Meteorology vol 77 no 3-4pp 191ndash205 1995

[90] T N Carlson andD A Ripley ldquoOn the relation between NDVIfractional vegetation cover and leaf area indexrdquo Remote Sensingof Environment vol 62 no 3 pp 241ndash252 1997

[91] M Minacapilli M Iovino and F Blanda ldquoHigh resolutionremote estimation of soil surface water content by a thermalinertia approachrdquo Journal of Hydrology vol 379 no 3-4 pp229ndash238 2009

[92] T R Karl G Kukla and J Gavin ldquoDecreasing diurnal temper-ature range in the United States and Canada from 1941 through1980rdquo Journal of Climate amp Applied Meteorology vol 23 no 11pp 1489ndash1504 1984

[93] G J Collatz L Bounoua S O Los D A Randall I Y Fungand P J Sellers ldquoA mechanism for the influence of vegetationon the response of the diurnal temperature range to changingclimaterdquo Geophysical Research Letters vol 27 no 20 pp 3381ndash3384 2000

[94] A Dai and C Deser ldquoDiurnal and semidiurnal variations inglobal surface wind and divergence fieldsrdquo Journal of Geophysi-cal Research D vol 104 no 24 pp 31109ndash31125 1999

[95] T J Jackson D M Le Vine A Y Hsu et al ldquoSoil moisturemapping at regional scales using microwave radiometry theSouthern great plains hydrology experimentrdquo IEEE Transac-tions on Geoscience and Remote Sensing vol 37 no 5 pp 2136ndash2151 1999

[96] T J Jackson A J Gasiewski A Oldak et al ldquoSoil moistureretrieval using the C-band polarimetric scanning radiometerduring the Southern Great Plains 1999 Experimentrdquo IEEETransactions on Geoscience and Remote Sensing vol 40 no 10pp 2151ndash2161 2002

[97] T J Jackson M H Cosh R Bindlish et al ldquoValidationof advanced microwave scanning radiometer soil moistureproductsrdquo IEEETransactions onGeoscience andRemote Sensingvol 48 no 12 pp 4256ndash4272 2010

[98] I Mladenova V Lakshmi T J Jackson J P Walker O Merlinand R A M de Jeu ldquoValidation of AMSR-E soil moisture usingL-band airborne radiometer data fromNational Airborne FieldExperiment 2006rdquo Remote Sensing of Environment vol 115 no8 pp 2096ndash2103 2011

[99] K E Mitchell D Lohmann P R Houser et al ldquoThe multi-institution North American Land Data Assimilation System(NLDAS) utilizing multiple GCIP products and partners in acontinental distributed hydrological modeling systemrdquo Journalof Geophysical Research D vol 109 no D7 article D07 2004

[100] B A Cosgrove D Lohmann K E Mitchell et al ldquoReal-timeand retrospective forcing in the North American Land DataAssimilation System (NLDAS) projectrdquo Journal of GeophysicalResearch D vol 108 no 22 pp 1ndash12 2003

[101] D Lohmann K EMitchell P R Houser et al ldquoStreamflow andwater balance intercomparisons of four land surface models inthe North American Land Data Assimilation Projectrdquo Journalof Geophysical Research vol 109 Article ID D07S91 2004

[102] D Lohmann K EMitchell P R Houser et al ldquoStreamflow andwater balance intercomparisons of four land surface models inthe North American Land Data Assimilation System projectrdquoJournal of Geophysical Research D vol 109 no 7 Article IDD07S91 2004

[103] A Robock L Luo E F Wood et al ldquoEvaluation of the NorthAmerican Land Data Assimilation System over the SouthernGreat Pkains during the warm seasonrdquo Journal of GeophysicalResearch vol 108 no D22 article 8846 2003

[104] J C Schaake Q Duan V Koren et al ldquoAn intercomparisonof soil moisture fields in the North American Land DataAssimilation System (NLDAS)rdquo Journal of Geophysical ResearchD vol 109 no 1 Article ID D01S90 2004

[105] E G Njoku T J Jackson V Lakshmi T K Chan andS V Nghiem ldquoSoil moisture retrieval from AMSR-Erdquo IEEE


Transactions on Geoscience and Remote Sensing vol 41 no 2pp 215ndash229 2003

[106] E G Njoku and S K Chan ldquoVegetation and surface roughnesseffects on AMSR-E land observationsrdquo Remote Sensing ofEnvironment vol 100 no 2 pp 190ndash199 2006

[107] T J Jackson ldquoIII Measuring surface soil moisture using passivemicrowave remote sensingrdquoHydrological Processes vol 7 no 2pp 139ndash152 1993

[108] C O Justice J R G Townshend E F Vermote et al ldquoAnoverview of MODIS Land data processing and product statusrdquoRemote Sensing of Environment vol 83 no 1-2 pp 3ndash15 2002

[109] C J Tucker ldquoRed and photographic infrared linear combina-tions for monitoring vegetationrdquo Remote Sensing of Environ-ment vol 8 no 2 pp 127ndash150 1979

[110] R B Myneni F G Hall P J Sellers and A L Marshak ldquoInter-pretation of spectral vegetation indexesrdquo IEEE Transactions onGeoscience and Remote Sensing vol 33 no 2 pp 481ndash486 1995

[111] R BMyneni S Hoffman Y Knyazikhin et al ldquoGlobal productsof vegetation leaf area and fraction absorbed PAR from year oneofMODIS datardquoRemote Sensing of Environment vol 83 no 1-2pp 214ndash231 2002

[112] R S De Fries M Hansen J R G Townshend and R SohlbergldquoGlobal land cover classifications at 8 km spatial resolution theuse of training data derived from Landsat imagery in decisiontree classifiersrdquo International Journal of Remote Sensing vol 19no 16 pp 3141ndash3168 1998

[113] Z Wan and Z L Li ldquoA physics-based algorithm for retrievingland-surface emissivity and temperature from EOSMODISdatardquo IEEE Transactions on Geoscience and Remote Sensing vol35 no 4 pp 980ndash996 1997

[114] R A McPherson C A Fiebrich K C Crawford et alldquoStatewide monitoring of the mesoscale environment a techni-cal update on the Oklahoma Mesonetrdquo Journal of Atmosphericand Oceanic Technology vol 24 no 3 pp 301ndash321 2007

[115] J L Heilman and D G Moore ldquoEvaluating near-surface soilmoisture using heat capacity mapping mission datardquo RemoteSensing of Environment vol 12 no 2 pp 117ndash121 1982

[116] S Hong V Lakshmi E E Small and F Chen ldquoThe influenceof the land surface on hydrometeorology and ecology newadvances from modeling and satellite remote sensingrdquo Hydrol-ogy Research vol 42 no 2-3 pp 95ndash112 2011

[117] Y Du F T Ulaby and M C Dobson ldquoSensitivity to soilmoisture by active and passive microwave sensorsrdquo IEEETransactions on Geoscience and Remote Sensing vol 38 no 1pp 105ndash114 2000

[118] T J Jackson and T J Schmugge ldquoVegetation effects on themicrowave emission of soilsrdquo Remote Sensing of Environmentvol 36 no 3 pp 203ndash212 1991

Submit your manuscripts athttpwwwhindawicom

Forestry ResearchInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Environmental and Public Health

Journal of



EcosystemsJournal of


MeteorologyAdvances in

EcologyInternational Journal of


Marine BiologyJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Advances in


Environmental Chemistry

Atmospheric SciencesInternational Journal of



Waste ManagementJournal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics


Geological ResearchJournal of

EarthquakesJournal of


BiodiversityInternational Journal of


ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


ClimatologyJournal of

2 ISRN Soil Science

estimate large-scale soilmoisture for the purpose ofmodelingthe interactions between land and atmosphere helping us tomodel weather and climate with higher accuracy

Recognizing the need for soil moisture observationson large spatial scales for continental scale hydrologicalmodeling investigators in the 1980s used the special sensormicrowave Imager (SSMI) [13] and scanning multichan-nel microwave radiometer [14] data sets The SMMR datahas been used to study soil moisture retrievals sensitivityand scaling on continental scales [15ndash17] The SSMI [1518] has been used in catchment scale studies [19] and incontinental scale studies in conjunction with a hydrologicalmodel [20ndash22] More recently successful retrieval has beencarried out using several missions including WindSat [23]tropical rainfall measuring mission (TRMM) microwaveimager (TMI) [24] Recently a promising soil moisture dataset has been developed jointly by researchers of NASAGoddard Space Flight Center and the Vrije UniversiteitAmsterdam [25] It utilizes C-band AMSR-E microwavebrightness temperatures in a Land Parameter RetrievalModel (LPRM) to obtain soil moisture This product hasbeen tested in a series of validation studies (eg [26ndash28])and shows high correlations with field observations [29]The Global Change Observation Mission-Water (GCOM-W) launched by the Japanese Space Agency is now pro-viding us with microwave data sets of the land surface[30]

Active and passive microwave remote sensing providesa unique capability to obtain observations of soil moistureat global and regional scales that help satisfy the scienceand application needs for hydrology [31ndash33] The emissiveand scattering characteristics of soil surface depend on soilmoisture among other variables that is surface temperaturesurface roughness and vegetation The electromagneticresponse of the land surface is modified by soil moisture andmodulated by surface roughness vegetation canopy effectsand interaction with the atmosphere before being received bya sensor These ancillary (non-soil-moisture) effects increaseat higher frequencies making low-frequency observationsdesirable for observation of soil moisture [34 35] Longerwavelengths also sense deeper soil layers (2ndash5 cm) at the L-band the penetration depth being of the order of one tenth ofthe wavelength [36] Retrieval of soil moisture using ground-based or aircraft-mounted radiometer operating at the L-band has been demonstrated in several prior studies [37ndash42]

An important experiment for investigating the capabilityof the PALS (passive and active L-and S-bands radiome-terradar) was conducted in the Southern Great Plainsregion of United States in July 1999 The SGP99 experimentsincluded bare pasture and agricultural crop surface coverwith field averaged vegetation water contents mainly in the0ndash25 kgmminus2 range Studies based on the SGP99 experimentsexhibited varying soil moisture retrieval potential with a 2-3 accuracy using passive channels and 2ndash5 accuracy usingthe active channels of the PALS instrument [40 42] Therewas a need to conduct similar studies under higher vegetationwater contents in order to evaluate the performance of soilmoisture retrieval algorithms under these conditions

The Soil Moisture Experiments in 2002 SMEX02 wereconducted in Iowa over a one-month period between mid-June and mid-July 2002 A major focus of SMEX02 wasextension of instrument observations and algorithms tomorechallenging vegetation conditions and understanding theimplications on soil moisture retrieval In situ measure-ments of gravimetric soil moisture soil temperature soilbulk density and vegetation water content were carried outcoincidently with PALS observations in active and passivechannels This study evaluated the performance of existingalgorithms andmodels for soil moisture retrieval using activeand passivemeasurements under themoderately high to veryhigh vegetation water content conditions

Field scale validation networks exist in Oklahoma [43ndash45] Illinois [46] and the USDA operational SCAN (SoilClimate Analysis Network) [47] Soil observing networkshave their challenges especially when used to validate satellitedata sets [48]

The NASA soil moisture active passive (SMAP) mission[53] is set for launch in 2014 SMAP will utilize a very largeantenna and combined radiometerradar measurements toprovide soil moisture at higher resolutions than radiometersalone can currently achieve SMAP [53] consists of bothpassive and activemicrowave sensorsThe passive radiometerwill have a nominal spatial resolution of 36 km and the activeradar will have a resolution of 1 km The active microwaveremote sensing data can provide a higher spatial resolu-tion observation of backscatter than those obtained froma radiometer (order of magnitude radiometer sim40 km andradar sim1 km or better) Radar data are more strongly affectedby local roughness microscale topography and vegetationthan a radiometer meaning that it is difficult to invertbackscatter to soil moisture accurately thus limiting thedevelopment of such algorithmsTherefore it can be difficultto use radar data alone SMAP will use high-resolution radarobservations to disaggregate coarse resolution radiometerobservations to produce a soil moisture product at 3 km reso-lution The soil moisture has been retrieved from radiometerdata successfully using various sensors and platforms andthese retrieval algorithms have an established heritage [3154]

There have been methods integrating the use of activesensors that have a higher spatial resolution to downscalepassive microwave soil moisture retrievals [55ndash57] Recentstudies have addressed the soil moisture downscaling prob-lem using MODIS sensor derived temperature vegetationand other surface ground variables The major publicationsin this area of study include the following (i) A methodbased on a ldquouniversal trianglerdquo concept was used to retrievesoil moisture from Normalized Difference Vegetation Index(NDVI) and land surface temperature (LST) data [50] (ii)A relationship between fractional vegetation cover and soilevaporative efficiency was explored for catchment studies inSoutheastern Australia by Merlin et al 2010 [49] while Mer-lin et al 2008 [51] developed a simple method to downscalesoil moisture by using two soil moisture indexes evaporativefraction (EF) and the actual EF (AEF) [58] (iii) A sequentialmodel which used MODIS as well as ASTER (AdvancedScanningThermal Emission andReflection Radiometer) data

ISRN Soil Science 3








119877

2


Merlin et al [51]















119861


119904


119879

119861

= 119890119879

119904

(1)



119890 = 1 minus 119903 (2)


119903

119907

=

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

120576

119903

cos 120579 minus radic120576119903

minus sin2120579

120576

119903

cos 120579 + radic120576119903

minus sin2120579

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

2

119903

ℎ

=

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

cos 120579 minus radic120576119903

minus sin2120579

cos 120579 + radic120576119903

minus sin2120579

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

2

(3)




4 ISRN Soil Science



119903

119901

= [119876119903

0119902

+ (1 minus 119876) 119903

0119901


where 1199030119902

and 1199030119901




119861


119879

119901

119861

= 119890

119901

119879

119904

exp (minus120591) + 119879119862

[1 minus exp (minus120591)] [1 + 119903119901

exp (minus120591)] (5)

where 119879119862



119901

and 119903119901


119890


119903 =

119887119882

119890

cos 120579

(6)




119872 = 1 minus 119890

minus120583LAI (7)


119879

119861

= 119872119879

canopy119861

+ (1 minus119872)119879

bare119861

(8)






ISRN Soil Science 5











119892


119892



6 ISRN Soil Science





119892

)lowast 001)











ISRN Soil Science 7








(LVV)119877

(SVV)119877

(LVV)119877









8 ISRN Soil Science








Soil







119888




L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136



ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics














ISRN Soil Science 3








119877

2


Merlin et al [51]















119861


119904


119879

119861

= 119890119879

119904

(1)



119890 = 1 minus 119903 (2)


119903

119907

=

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

120576

119903

cos 120579 minus radic120576119903

minus sin2120579

120576

119903

cos 120579 + radic120576119903

minus sin2120579

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

2

119903

ℎ

=

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

cos 120579 minus radic120576119903

minus sin2120579

cos 120579 + radic120576119903

minus sin2120579

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

1003816

2

(3)




4 ISRN Soil Science



119903

119901

= [119876119903

0119902

+ (1 minus 119876) 119903

0119901


where 1199030119902

and 1199030119901




119861


119879

119901

119861

= 119890

119901

119879

119904

exp (minus120591) + 119879119862

[1 minus exp (minus120591)] [1 + 119903119901

exp (minus120591)] (5)

where 119879119862



119901

and 119903119901


119890


119903 =

119887119882

119890

cos 120579

(6)




119872 = 1 minus 119890

minus120583LAI (7)


119879

119861

= 119872119879

canopy119861

+ (1 minus119872)119879

bare119861

(8)






ISRN Soil Science 5











119892


119892



6 ISRN Soil Science





119892

)lowast 001)











ISRN Soil Science 7








(LVV)119877

(SVV)119877

(LVV)119877









8 ISRN Soil Science








Soil







119888




L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136



ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics














4 ISRN Soil Science



119903

119901

= [119876119903

0119902

+ (1 minus 119876) 119903

0119901


where 1199030119902

and 1199030119901




119861


119879

119901

119861

= 119890

119901

119879

119904

exp (minus120591) + 119879119862

[1 minus exp (minus120591)] [1 + 119903119901

exp (minus120591)] (5)

where 119879119862



119901

and 119903119901


119890


119903 =

119887119882

119890

cos 120579

(6)




119872 = 1 minus 119890

minus120583LAI (7)


119879

119861

= 119872119879

canopy119861

+ (1 minus119872)119879

bare119861

(8)






ISRN Soil Science 5











119892


119892



6 ISRN Soil Science





119892

)lowast 001)











ISRN Soil Science 7








(LVV)119877

(SVV)119877

(LVV)119877









8 ISRN Soil Science








Soil







119888




L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136



ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics














ISRN Soil Science 5











119892


119892



6 ISRN Soil Science





119892

)lowast 001)











ISRN Soil Science 7








(LVV)119877

(SVV)119877

(LVV)119877









8 ISRN Soil Science








Soil







119888




L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136



ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics














6 ISRN Soil Science





119892

)lowast 001)











ISRN Soil Science 7








(LVV)119877

(SVV)119877

(LVV)119877









8 ISRN Soil Science








Soil







119888




L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136



ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics














ISRN Soil Science 7








(LVV)119877

(SVV)119877

(LVV)119877









8 ISRN Soil Science








Soil







119888




L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136



ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics














8 ISRN Soil Science








Soil







119888




L band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 07136



ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics














ISRN Soil Science 9



S band average BT

230

240

250

260

270

280

290

300

230 240 250 260 270 280 290 300Observed

Sim

ulat

ed

1198772 = 0577


0

01

02

03

04

05

0 01 02 03 04 05In situ

Retr

ieve

d

119910 = 09718119909 + 00013

1198772 = 07134


(119879119861
















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics






















119879

119861119901

= 119860 + 119861119898

119907

(9)

120590

0

119901119901

= 119862 + 119863119898

119907

(10)


119907119907


Δ120590

0

119901119901

= 119863Δ119898

119907

(11)

Δ120590

0

119901119901


119907




0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics
















0

)

119863

119863

0

= 119891 (120591) (12)


Δ120590

0

119901119901

= 119891 (120591)119863

0

Δ119898

119907

(13)



Δ119898

119907

=

Δ120590

0

119878

0

(14)

where 1198780

= 119891(120591)lowast119863

0


119907119909


0

to 1199050

+ Δ119905 given119898119907X 1205900

119909

and 120591119909

at times 1199050

and 1199050


Δ119898

119907X =1

119873

sum119898

119907119909

(15)



119907119909



Δ119898

119907X =1

119873

sum

Δ120590

0

119909

119878

0

(16)

The unknown 1198780


0


119878

0

=

1

119873

sumΔ120590

0

119909

Δ119898

119907X (17)

119878

0


Δ119898

119907119909

=

Δ120590

0

119909

119878

0

(18)



0






01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















01 02 03 04119898119907

120590HH

minus10

minus20

minus30

minus40

minus50

minus60

minus70

119904 = 24

119904 = 12

119904 = 06

119904 = 05119904 = 04

119904 = 03

119904 = 01


119907




0

1

2

3

4

5

6


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2248119909 + 0231

minus2

minus1

minus01

1198772 = 057




0



0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2223119909 + 11

minus2minus01

1198772 = 038


0

2

4

6

8

10


Cha

nge

in ra

dar b

acks

catte

r (dB

)

119910 = 2376119909 + 071

minus2

minus4

minus01

1198772 = 039











119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics
















119909















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics





















Field Δ120579 Δ120590





423 Methodology


NDVI119889

= 119886

0

sin (1198861

lowast 119863 + 119886

2

) + 119886

3

(19)

where 1198860

119886

1

119886

2

and 1198863












119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics























119868 = radic119896120588119888(20)




119904


can bedescribed as

Δ119879

119904

= 119879max minus 119879min (21)



119904



119904



119901



119904






Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics
















Day Dataset 119877

2














lowast

lt0001















lowast

lt0001



119904


120579

av(119894 119895) = 119886

0

+ 119886

1

Δ119879

119904

(119894 119895) (22)


44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















44 445 45 455 46 465

times104

4646

4642

44 445 45 455 46 465

times104

4646

4642

15

minus36

Δ119879119861

(K)


44 445 45 455 46 465

44 445 45 455 46 465

times104

times104

4646

4642

4646

4642

23

minus5


Δ120590

(dB)


1

3

5

7

0 10 20minus3

minus1


119910 = minus02458119909 minus 01508

1198772 = 08112

Δ119879119861 (K)

Δ120590

(dB)




and 1198861




119904




120579

avc(119894 119895) = 120579

av(119894 119895) +

[

[

(

Θ

119886

+ Θ

119901

2

) minus

1

119873

sum

119894119895

120579

av(119894 119895)

]

]

(23)



0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 101119909 + 00001

1198772 = 072

minus01


(a)

0

01

02

03

04

0 01 02

In situ

Pred

icte

d

minus02

minus03

minus01

minus04

minus02minus03

119910 = 102119909 + 00045

1198772 = 085

minus01


(b)


0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


119910 = 098119909 + 0004

1198772 = 068

minus01


(a)

0

01

02

03

0 01 02 03

In situ

Estim

ated

minus02

minus03


1198772 = 085

minus01

119910 = 094119909 minus 0003


(b)



424 Results

(1) 120579av minus Δ119879119904




102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

98∘15998400W 98∘6998400W 97∘57998400W 97∘48998400W

34∘57998400N

34∘48998400N

34∘57998400N

34∘48998400N

0 35 70 140 210 280(km)

(km)0 2 4 8 12 16

N

EW

S

N

EW

S








102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(a) Day 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N

325316307298289280

(K)

(b) Night 119879119904

from July 21 2005

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)


102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

02

04

06

08

1



AMSR-E gridded data

1 km MODIS pixel

186∘ NLDAs pixel

Θ119886 Θ119901

NDVI(119894119895)

14∘

120579119886(119894 119895) 120579119901(119894 119895)120579av (119894 119895)

119879119904(119894 119895) Δ119879119904(119894 119895)

(a)


Δ119879119904(119894119895)

120579av(119894119895)

(b)






00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)


May

00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

August


00


005

01

015

02

025

03

035

04

Dai

ly av

erag

e soi

l moi

sture

(m3m3)

0 lt NDVI lt 03

July







102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

(i) (ii)

(iii) (iv)

(v) (vi)

(a)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)

102∘W 100∘W 98∘W 96∘W

102∘W 100∘W 98∘W 96∘W

36∘N

34∘N

36∘N

34∘N0

008

016

024

032

04

(m3m3)




av

from August 9 2005

(b)















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics



























0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 050450350250150050

00501

01502

02503

03504

04505

0005

01015

02025

03035

04045

05





1198772 = 0161

RMSE = 0114

1198772 = 036

RMSE = 0128

1198772 = 0264

RMSE = 0108


1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

(a)

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0

RMSE = 0161198772 = 002

RMSE = 0168

1198772 = 0005

RMSE = 0099

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005


(b)

Figure 20 Continued


0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05

0005

01015

02025

03035

04045

05





1198772 = 0005

RMSE = 01461198772 = 001

RMSE = 0167

1198772 = 0006

RMSE = 0103

1 km

dow

nsca

led

soil

moi

sture

(m3m3)

AM

SR-E

soil

moi

sture

(m3m3)

NLD

AS

soil

moi

sture

(m3m3)

Estim

ated

soil

moi

sture

(m3m3)

0 01 02 03 04 05045035025015005 0 01 02 03 04 05045035025015005

0 01 02 03 04 050450350250150050 01 02 03 04 05045035025015005


(c)







0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)




0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)








0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



m d

owns

cale

d so

il m

oistu

re (m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



AM

SR-E

soil

moi

sture

(m3m3)

0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05



NLD

AS

soil

moi

sture

(m3m3)


0

005

01

015

02

025

03

035

04

045

05

0 005 01 015 02 025 03 035 04 045 05


Estim

ated

soil

moi

sture

(m3m3)









0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics















0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)



2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (AMSR-E)


0

2

4

6

8

10

12

14

16

minus02 0 005 01 015 02

(m3m3)

May 2004 (NLDAS)



References


































































































































Journal of












Volume 2014

Advances in









Geophysics



































































































































Journal of












Volume 2014

Advances in









Geophysics














review article remote sensing of soil...

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