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Microwave remote sensingof soil moisture forestimation of profile soilpropertyN. M. Mattikalli , E. T. Engman , L. R. Ahuja &T. J. JacksonPublished online: 25 Nov 2010.

To cite this article: N. M. Mattikalli , E. T. Engman , L. R. Ahuja & T. J. Jackson(1998) Microwave remote sensing of soil moisture for estimation of profilesoil property, International Journal of Remote Sensing, 19:9, 1751-1767, DOI:10.1080/014311698215234

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int. j. remote sensing, 1998, vol. 19, no. 9, 1751 ± 1767

Microwave remote sensing of soil moisture for estimation of pro® le

soil property

N. M. MATTIKALLI and E. T. ENGMANNASA Goddard Space Flight Center, Laboratory for Hydrospheric Processes,Hydrological Sciences Branch, Code 974, Greenbelt, MD 20771, USA

L. R. AHUJAUSDA Agricultural Research Service, Great Plains Systems Research,301 South Howes, PO Box E, Fort Collins, CO 80522, USA

and T. J. JACKSONUSDA Agricultural Research Service, Hydrology Laboratory, Beltsville,MD 20705, USA

(Received 30 September 1996; in ® nal form 11 July 1997 )

Abstract. Multi-temporal microwave remotely-sensed soil moisture has beenutilized for the estimation of pro® le soil property, viz. the soil hydraulic conduc-tivity. Passive microwave remote sensing was employed to collect daily soilmoisture data across the Little Washita watershed, Oklahoma, during 10± 18 June1992. The ESTAR (Electronically Steered Thin Array Radiometer) instrumentoperating at L -band was ¯ own on a NASA C-130 aircraft. Brightness temperature(TB ) data collected at a ground resolution of 200 m were employed to derivespatial distribution of surface soil moisture. Analysis of spatial and temporal soilmoisture information in conjunction with soils data revealed a direct relationbetween changes in soil moisture and soil texture. A geographical informationsystem (GIS) based analysis suggested that 2-days initial drainage of soil, measuredfrom remote sensing, was related to an important soil hydraulic property viz. thesaturated hydraulic conductivity (Ksat ). A hydrologic modelling methodology wasdeveloped for estimation of Ksat of surface and sub-surface soil layers. Speci® cally,soil hydraulic parameters were optimized to obtain a good match between modelestimated and ® eld measured soil moisture pro® les. Relations between 2-days soilmoisture change and Ksat of 0± 5 cm, 0± 30 cm and 0± 60 cm depths yielded correla-tions of 0 7́8, 0 8́2 and 0 7́1, respectively. These results are comparable to the® ndings of previous studies involving laboratory-controlled experiments andnumerical simulations, and support their extension to the ® eld conditions of theLittle Washita watershed. These ® ndings have potential applications of microwaveremote sensing to obtain 2-days of soil moisture and then to quickly estimate thespatial distribution of Ksat over large areas.

1. Introduction

Soil moisture is important to the hydrologic research for partitioning rainfallinto run-o� and in® ltration components as well as separating incoming solar radi-ation into latent and sensible heat. Remotely-sensed data have a great potential forproviding areal estimates of soil moisture. Although remote sensing of soil moisturecan be accomplished to some degree or other by all regions of the electromagneticspectrum, only the microwave region o� ers truly quantitative measurements

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(Engman and Gurney 1991) because the primary physical property that a� ects themeasurement is directly dependent on the amount of water present in the soil(Schmugge et al. 1986). The distinct advantage of microwave sensors is their capabil-ity to penetrate clouds, and to a signi® cant extent, the vegetation canopy. Passivemicrowave remote sensing employs measurements of the thermal emission from thesoil to determine the moisture content in the surface layer of the soil. It relies on thefact that the emissivity (e) at microwave wavelengths is a function of the dielectricconstant of the soil-water mixture and thus the soil moisture. The real part ofdielectric constant is about 80 for water while for dry soil it is less than 5. Therefore,for soils it ranges from about 3 5́ to over 20 between dry and wet conditions. Thisproduces a change in e from 0 9́5 to less than 0 6́ for dry and wet soils, respectively.This decrease in e is approximately linear with soil moisture and is a� ected by factorssuch as soil texture, surface roughness, and vegetation cover. The texture a� ects theslope of the relation between e and soil moisture but not the range of variation.While both roughness and vegetation reduce the range of variation, vegetation ismore signi® cant because it can totally obscure the soil surface if it is present insu� ciently large amounts. This will occur for vegetation water contents in excess of6 to 7 kg m Õ

2 at the 21 cm wavelength (Jackson and Schmugge 1991 ).The relation between microwave emission of natural surfaces and their inherent

moisture content has been studied and well documented in the literature (Schmuggeet al. 1986, 1992, Jackson 1988 ). Studies involving truck and aircraft measurementsnot only demonstrated this basic relation but have also helped to quantify the e� ectsof various surface parameters such as soil texture, roughness and vegetation thatdistort and confound the basic relation (Ulaby et al. 1982, Theis et al. 1984, Jacksonand Schmugge 1991, Engman and Chauhan 1995). A few studies have made temporalobservations to map spatial variation in soil moisture (Engman et al. 1989, Wanget al. 1989).

Soil physical and hydraulic properties are of prime interest for water and energybalance studies and simulations of land surface atmosphere interaction at variousscales. Although soil properties are typically measured in the ® eld at the point scale,such data are unavailable over large areas, or have limitations in accounting regionalspatial variability, or prohibitively expensive, tedious and time consuming to coverlarge areas. Methods based on remote sensing provide alternative tools to obtainquick estimates of soil properties. For example, soil hydraulic parameters at a plotscale can be estimated using microwave brightness temperature (TB ), thermal infrareddata and inversion of soil hydrology models (van de Griend and O’Neill 1986).Remotely-sensed TB may be inverted to estimate soil hydraulic properties using amicrowave emission model and soil moisture and temperature pro® les generated bymoisture and energy balance equations (Camillo et al. 1986). Application of suchapproaches on a regional scale may generate large-scale soil properties for inputinto mesoscale land-atmosphere models. Regional soil properties may be estimatedby inversion of dynamic one-dimensional soil-water-vegetation model in conjunctionwith soil moisture obtained from microwave remote sensing, and evaporation derivedfrom re¯ ective and thermal infrared remote sensing (Feddes et al. 1993). Theseapproaches probably yield promising results if all input data requirements aresatis® ed. Nevertheless, such approaches are not ideally suited over areas havinglimited or no data on hydrologic and meteorological parameters. A recent study byAhuja et al. (1993) indicates that average surface and pro® le hydraulic properties(viz. the saturated hydraulic conductivity, Ksat ) can be estimated using changes in

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Soil property estimation f rom remotely-sensed soil moisture 1753

moisture content of the surface soil, 2-days after a thorough wetting. These ® ndingswere based on both controlled laboratory experiment on selected soils and numericalsimulations. Such relations are valuable to obtain estimates of average Ksat fromremotely-sensed soil moisture observations made at 2-days temporal resolution.

The objective of this paper was to test the ® ndings of Ahuja et al. (1993) to the® eld conditions of the Little Washita watershed, Oklahoma. Speci® cally, the aim ofthis study was to develop relations between 2-days changes in surface soil moistureobtainable from microwave remote sensing and surface and pro® le soil hydraulicproperty viz. the Ksat . Ksat is an important soil property that is di� cult to obtainother than in a laboratory (Hanks 1992), and being a distributed point sample maynot be a representative of the real Ksat in the ® eld. Therefore, a method based onremote sensing that has capabilities of deriving spatial distribution of Ksat would bean extremely important data source for hydrologic applications.

2. Study area and data description

The study area is the Little Washita watershed of 610 km2 drainage basin situatedin the southern part of the Great Plains in south-west Oklahoma. A researchexperiment (viz. Washita ’92) was carried out in the watershed during 10± 18 June1992. Climate of the Little Washita region is moist and sub-humid with an averageannual rainfall of about 750 mm (Allen and Naney 1991). During the experiment,land cover in the watershed was dominated by pasture and senesced or harvestedwinter wheat with some other agricultural crops including corn and alfalfa (Jacksonand Schiebe 1993). Some patches of bare soil were observed mainly due to harvestingof wheat. The forest cover within the watershed is very sparse typically followingstreams and constitutes a small proportion of the watershed. As part of the Washita’92 campaign, multi-temporal airborne microwave data were collected using theElectronically Steered Thinned Array Radiometer (ESTAR) (Jackson and Schiebe1993). Table 1 lists the key parameter values for the ESTAR instrument during thedata collection. ESTAR is a synthetic aperture, passive microwave radiometer whichoperates at L -band (21 cm wavelength, or 1 4́ GHz frequency) (Le Vine et al. 1990).This band has been proved to be the most e� ective for measuring soil moisture(Schmugge et al. 1986). It is well established on both experimental and theoreticalwork that the soil moisture sampling depth is of the order of a few tenths of thewavelength in the soil. For the 21 cm wavelength ESTAR, this translates to a depthof about 2± 5 cm in the soil. Spatial resolution of the data collected during Washita’92 was 200 m.

In addition to the aircraft measurements, a large number of ground soil moisturemeasurements (more than 700 gravimetric samples per day) were carried out. Theseground truth data were used to support and validate microwave remotely-sensed

Table 1. Key parameter values for ESTAR instrument during Washita ’92 data collection.

Parameter Value

1. Centre frequency 1 4́ GHz2. Polarization Horizontal3. Resolution Ô 4 ß (both along and cross-track)4. Swath width Ô 45 ß5. Band width 25 MHz6. Integration time 0 2́5 seconds

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N. M. Mattikalli et al.1754

Figure 1. Illustration of temporal variations of average ® eld measured surface soil moisturefor di� erent soil textures found in the Little Washita watershed. Since there was norainfall during the Washita ’92 experiment, the study area experienced a clear dry-down from very wet to dry over a period of nine days.

data. Pro® le soil moisture was measured at thirteen sites using a Resonant FrequencyCapacitance (RFC) probe beginning at 7 5́ cm below the soil surface and at 15 cmintervals up to a depth of 115 cm. Soil temperatures were collected daily at each soilmoisture sampling site at depths of 5 cm and 15 cm using a metal dial type thermo-meters as well as digital probes, and the sampling showed little temporal or spatialvariability. The study area experienced heavy rainfall (more than 30 mm) on 5 June1992, and moderate rainfall continued till 9 June 1992. However, there was norainfall in the watershed for the entire duration of the Washita ’92 experiment(Jackson and Schiebe 1993). Therefore, the hydrologic conditions in the watershedwere ideal because it was possible to follow a drying period from very wet to dry (achange in volumetric soil moisture content from about 30 to 10 per cent) over aperiod of nine days ( ® gure 1).

3. Brightness temperature and soil moisture variability

3.1. Brightness temperature variabilityFigure 2 shows maps of daily TB (expressed in deg K ) during 10± 18 June 1992

(except 15 June 1992, the aircraft crew rest day). These maps clearly portray boththe spatial and temporal variation of TB across the watershed. Referring to the mapof 10 June 1992, the watershed can be spatially partitioned into regions of lowtemperatures (a minimum of 190 deg K) on the eastern and western parts which areseparated by a region of comparatively higher temperatures (a maximum of250 deg K). On the last day the spatial variation is enhanced and can be observedmore clearly. The temperature in the eastern and western parts has increased drastic-ally (a new minimum of 230 deg K), so as the temperature in the middle region (anew maximum around 270 deg K). An overall domination of cold temperature on10 June 1992, has changed to a domination of warm temperature on 18 June 1992.

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Soil property estimation f rom remotely-sensed soil moisture 1755

Figure 2. Temporal maps of daily TB ( in deg K) acquired by ESTAR during 10± 18 June 1992(15 June was the aircraft crew rest day). Spatial variation of TB closely follows thespatial distribution of soil texture (see ® gure 5).

This spatial and temporal variability is an interesting observation, and will be furtheranalysed using maps of soil moisture.

3.2. Soil moisture retrievalMicrowave TB data were employed to generate temporal surface soil moisture

information, and ® gure 3 shows various stages of the algorithm (see Jackson 1993for a detailed formulation of this algorithm). In the initial stage, the land surfacewas categorized into forest/dense vegetation, snow/ice, broadleaf vegetation, stalkdominated vegetation, or bare soil. Land cover types (for e.g., urban, quarries, airportrunways) not suitable were thus excluded from further processing. Although soiltemperature sampling during the experiment showed little variation, a value of23 5́ ß C was used to convert TB into emissivity, e, using:

TB= eTsoil (1 )

where Tsoil is the physical temperature of the soil. Emissivity is not sensitive to Tsoil ,

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Figure 3. Soil moisture retrieval algorithm (adapted from Jackson 1993).

however, these data were obtained close to the microwave data acquisition.Atmospheric contributions to TB were neglected since they are generally small at the21-cm wavelength. The next step is correction for vegetation e� ects using the methodoutlined in Jackson and Schmugge (1991). The correction requires an estimate ofvegetation type and water content. These parameters were determined using the landuse/land cover data base (Jackson et al. 1995). Additional information was derivedfrom a standard normalized di� erence vegetation index (NDVI) equation using theSPOT multi-spectral radiance data collected on 3 July 1992. Visual analysis of theNDVI image indicated that four general levels were present corresponding to novegetation, winter wheat, corn, and rangeland/pasture (Jackson et al. 1995). It wasassumed that a single vegetation water content value could be used to representeach of these categories. Ground observations made during the experiment wereused to assign a vegetation water content to each level. Surface roughness e� ect wascorrected using the model described by Choudhury et al. (1979), which requires ane� ective roughness parameter (h). Based on ® eld sampling of a range of conditions,a value of 0 1́ was used, which is representative of smooth soil conditions. Finally,

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Soil property estimation f rom remotely-sensed soil moisture 1757

the volumetric soil moisture was determined from soil emissivity by ® rst invertingthe Fresnel equations to determine an e� ective dielectric constant for the near-surfacesoil layer, and then using dielectric mixing model relations and soil texture properties(viz. percentage sand, percentage clay and bulk density) to estimate soil moisture(Jackson 1993 ).

Jackson et al. (1995) conducted veri® cation studies using ground-based surfacesoil moisture measurements at nine small sites before applying the soil moistureretrieval algorithm for the entire watershed. Surface conditions ranged from baresoil to rangeland, and for each site approximately sixteen gravimetric samples weretaken. The ESTAR data used for initial veri® cation were from the near-nadir beampositions collected during low altitude passes over the ground sites. The soil moisturealgorithm was used to predict the soil moisture± TB relation for several conditionsthat were typical of the watershed (Jackson et al. 1995). The correspondence betweenobserved and predicted values for the bare and sparse vegetation ® elds was verygood, as were the corn ® eld observations. However, the rangeland data exhibitedsmall variation for both soil moisture and TB , which made veri® cation di� cult. Thestandard error of estimates for the two cover groups of ® elds were 3 5́ per cent forthe bare and 5 7́ per cent for the vegetated (Jackson et al. 1995). O’Neill et al. (1994)report an average absolute error of less than 1 5́ per cent between predicted andmeasured soil moisture over a large corn ® eld, which represented the densest vegeta-tion cover encountered during the experiment (canopy height of 2 1́ m). The investig-ators derived vegetation parameters using a vegetation scattering model whichemployed truck-based radar data at 1 6́ GHz. Although these veri® cations suggestgood correlations, some di� erence is expected if comparisons are made at twodi� erent scales. For example, one might expect to have errors if coarse resolutionremotely-sensed soil moisture observations are compared to point scale ® eld measure-ments because of large variability of soil moisture within the footprint of the sensor.

3.3. Soil moisture variabilityFigure 4 shows multi-temporal soil moisture information derived from ESTAR

TB . This ® gure depicts both spatial and temporal variability of surface soil moistureon a daily time scale for the period 10± 18 June 1992. On 10 June 1992, wet and drysoils were characterized by volumetric moisture contents of about 35 and 15 percent, respectively. At the start of the experiment, surface soil was at near saturatedcondition (about 30 ± 35 per cent) in the eastern and western part of the watershed.This status of the soil is expected because of the heavy rainfall recorded on 5 June1992. The soil moisture available at the start of the experiment was lost mainly bysubsurface drainage and evapotranspiration to a small extent (Mattikalli et al. 1998 ).The dry down pattern of the soil is captured clearly in the soil moisture maps from11 to 18 June 1992. During this period, progressive loss and pronounced spatialvariability of soil moisture can be observed. At the end of the experiment, the rangeof soil moisture content varied from about 20 per cent (wet soil ) in the eastern andwestern regions to about 5 per cent (very dry soil ) in the central region. Theseobservations have been utilized to establish the relation between changes in soilmoisture and soil hydraulic property.

4. Relation between changes in soil moisture and soil texture

Figure 5 shows the map of soil texture for the Little Washita watershed. Thewatershed is dominated by silt loam and loam on both the eastern and western

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Figure 4. Temporal maps of daily surface soil moisture derived from ESTAR TB for each dayof the experiment: 10± 18 June 1992. Similar to the TB variability, the spatial variationof soil moisture also follows the soil texture distribution in the region.

Figure 5. Map of soil texture in the Little Washita watershed. Source: Map Information andDisplay Systems (MIADS) database (Allen and Naney 1991, Jackson and Schiebe 1993).

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Soil property estimation f rom remotely-sensed soil moisture 1759

regions, which are partitioned by ® ne sandy loam and sand. It is interesting toobserve the pattern of spatial distribution of soil texture in conjunction with thepatterns of spatial and temporal variation of both TB and soil moisture. It is evidentfrom these data sets that the spatial and temporal distribution of both TB and soilmoisture closely follow the distribution of soil texture in the watershed. This observa-tion is also obvious in ® gure 1 which shows the temporal variation of soil moistureof ® ve di� erent soil types. It is clear that both sandy soils and loamy soils havedistinct patterns of soil moisture contents and soil moisture drainage. A detailedanalysis carried out within a raster-based geographical information system (GIS)framework demonstrated that silt loam and loam soils are characterized by higherchanges of total moisture content, whereas sandy loam and sandy soils are associatedwith lower changes. These characteristics may be related to soil hydraulic properties,and it suggests that remotely-sensed soil moisture information may be valuable toidentify soil types and to estimate soil hydraulic properties (Mattikalli et al. 1998).

5. Relation between 2-days drainage and Ksat

Extensive research was carried out to establish relations between 2-days drainageand Ksat for the ® eld conditions of the Washita ’92 experiment. Figure 6 shows theschematic diagram of the steps involved in development of the relations. The maindi� culty encountered in this e� ort was the absence of data concerning pro® le soilproperties for the watershed. Therefore, pro® le Ksat values were derived using aninteractive numerical simulation and modelling of soil moisture redistributionthrough the sub-surface layers (shaded box in ® gure 6).

5.1. Retrieval of sub-surface soil propertiesModelling of soil moisture movement was carried out using a state-of-the-art

soil hydrology model, viz. the Root Zone Water Quality Model (RZWQM ) (Ahujaand Hebson 1992) and a raster-based GRASS GIS (US-CERL 1991). Spatial and

Figure 6. Schematic diagram of various steps for developing relationship between 2-daysinitial soil moisture change and pro® le Ksat values.

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multi-temporal data were georeferenced and stored in the GIS for easy manipulation,analysis and retrieval (Mattikalli et al. 1995). The physically based RZWQM simu-lates movement of water and its redistribution in unsaturated zone and accounts forall components of the water balance, evapotranspiration and drainage (Ahuja andHebson 1992). The model simulates in® ltration and macropore ¯ ow during precipita-tion events by layered and radial Green Ampt expressions, respectively. Soil moistureredistribution between events is simulated by ® nite di� erence solution to the Richard’sequation. Figure 7 shows the schematic diagram of the modelling approach. TheRZWQM simulations were carried out on a daily time step for one site at a timefor all thirteen sites where pro® le soil moisture data were measured. Figure 8 showsthe locations, and table 2 lists the surface soil texture and vegetation cover of thesesites. The model requires information on various meteorological parameters, initialconditions of soil moisture and temperature pro® les, and pro® le soil properties. Atsome sites (including RG132 and RG154) measurement of pro® le soil moisturestarted on 11 June 1992. The ® rst day soil moisture and temperature pro® les wereused as initial conditions, and the model was run to simulate daily soil moisture forthe remaining days (accordingly, the results will be presented from the ® rst day ofsimulation). Actual measured meteorological data concerning maximum and min-imum air temperature, wind speed, relative humidity, and pan evaporation were

Figure 7. Schematic diagram of the optimization approach developed for the estimation ofsoil hydraulic properties using the RZWQM.

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Soil property estimation f rom remotely-sensed soil moisture 1761

Figure 8. Locations of RZWQM simulation sites within the Little Washita region.

Table 2. Surface soil texture and Vegetation cover for the RZWQM simulation sites.

Soil texture (%)

Site Sand Silt Clay Vegetation cover

RG122 45 7́ 37 9́ 16 4́ Bermuda grass pastureRG123 45 0́ 45 3́ 9 7́ Weedy grass pastureRG130 59 8́ 28 4́ 11 8́ Not knownRG132 72 6́ 24 2́ 3 2́ Not knownRG133 59 9́ 33 4́ 6 7́ Not knownRG134 87 5́ 10 9́ 1 6́ Post oak rangelandRG136 59 2́ 32 9́ 7 9́ PastureRG137 56 7́ 31 8́ 11 5́ RangelandRG146 73 2́ 22 2́ 4 6́ PastureRG147 Ð Ð Ð Not knownRG148 45 1́ 43 7́ 11 2́ PastureRG152 70 6́ 21 9́ 7 5́ Bermuda grass pastureRG154 76 5́ 16 8́ 6 7́ Weedy grass pasture

input for the entire duration of simulation. As noted earlier, soil temperature wasmeasured at 5 cm and 15 cm depths, and it was assumed that temperature does notvary below 15 cm depth. The soil survey maps for the watershed provide generalizedsoil types for the region and some representative soil pro® le information. These datawere used to extract soil horizon descriptions and approximate soil texture informa-tion. The number of soil horizons varied typically between three and six. Soilhydraulic properties for these horizons and their soil texture types were extractedfrom the data compiled in Rawls and Brakensiek (1989). These approximate soilhydraulic properties were re® ned during the optimization algorithm.

The optimization algorithm consisted of employing approximate pro® le soilhydraulic properties in the initial model simulation to estimate the pro® le soilmoisture distributions for the duration of the experiment. Model derived soil moisturedistributions were sampled at various depths, and were analysed and compared withthose measured using the RFC probe for each day. Invariably, the ® rst run yieldedpoor match between modelled and ® eld measured soil moisture pro® les. In thesubsequent simulations, four important hydraulic properties viz. bulk density, onethird bar water content, Ksat and pore size distribution index were optimized toobtain improved match between modelled and ® eld measured moisture pro® les. Ineach model simulation, only one hydraulic property was optimized for each horizon,

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N. M. Mattikalli et al.1762

and the simulations were repeated to optimize the property for all horizons.Optimization process was repeated for all four soil properties at each site until thebest possible match is achieved between the modelled and ® eld measured soil mois-ture pro® les. Typically, the number of model iterations varied between 35 and 110per site, and ultimately resulted in a unique set of values for the four hydraulicproperties for all horizons of thirteen sites.

Figures 9(a) ± (c) show the ® nal results of the model simulations for three examplesites RG132, RG148 and RG154, although such results are available for the remainingten sites. As expected, it may be observed that the optimization technique yielded agood match between the model predicted and ® eld measured soil moisture variationsfor all depths, although some variation among remotely-sensed, ® eld measuredgravimetric and RFC probe and model predicted soil moisture is apparent for the0± 5 cm layer. For this layer, the model predicted soil moisture remains close togravimetric ® eld measurements, and remotely-sensed values and those derived fromRFC probe show consistent deviation from the remaining. This is because functioningof the RFC probe for the top soil layer (0 ± 5 cm) is questionable because of calibrationanomalies. Errors associated with remotely sensed observations may be attributedto di� erent scales of measurement. Field data are essentially point measurements atsampling sites whereas remotely-sensed data represent average soil moisture condi-tion over a foot print of 200 m by 200 m. However, the optimization yielded consist-ently good match between the model predicted and ® eld measured soil moisture fordeeper layers, which suggests that the unique set of soil hydraulic properties obtainedfor deeper layers are fair estimates of ® eld soil properties.

5.2. Establishing the relationsHarmonic-mean Ksat was calculated for each site and for three di� erent depths

using the model simulation results. These harmonic-mean Ksat values for 5 cm, 30 cmand 60 cm pro® les were related to the 2-days initial drainage of the surface soilmoisture ( ® gures 10 (a), (b), (c)) . At one site (viz., RG123) the depth to bed rock is44 cm, and accordingly there are only twelve data points in ® gure 10 (c). The 2-daysinitial soil moisture change was calculated as:

2-days initial change in soil moisture=Saturated soil moisture on 10 June 1992 (2 )Õ Soil moisture on 12 June 1992

Since the soil was not saturated at the start of the experiment on 10 June 1992, thesaturated soil moisture was calculated as:

Saturated soil moisture=0´9 Ö Soil porosity (3 )

The soil moisture for 12 June 1992 was that derived from ESTAR TB .The least squared linear regression equations presented in ® gure 10 suggest that

pro® le harmonic-mean Ksat bears a good positive relation with 2-days changes insurface soil moisture (with correlation coe� cients of 0 7́8, 0 8́2 and 0 7́1 for 5 cm,30 cm and 60 cm depths, respectively) . These results are in close agreement withthose reported in literature. Earlier studies have reported log-log linear regressioncoe� cients ranging from 0 1́7 to 0 7́3 for various types of soils drained undercontrolled laboratory conditions, and 0 9́7 for the theoretical data (Ahuja et al. 1993).Similar results between pro® le Ksat and porosity for various depths between 15 to60 cm were also reported elsewhere (Ahuja et al. 1984).

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(a)ii

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(c)i (c)ii

(c)iv(c)iii

Figure 9. Results of the hydrologic model simulations for the estimation of pro® le soil hydraulicproperties for sites (a) RG132; (b) RG148; and (c) RG154. Model predicted soil moisturevariations are shown in solid and dotted lines, whereas the ® eld measured values areshown with symbols. Numbers in the legend indicate depths below the surface.

The results have potential signi® cance for derivation of quick estimates of thespatial distribution of Ksat , typically not measured in the ® eld, from microwaveremote sensing of soil moisture. However, these results need to be tested for general-ization and universal applicability for various other regions.

6. Conclusion

Microwave remote sensing was employed to obtain spatial and multi-temporalsoil moisture data for the Little Washita watershed, Oklahoma. Analysis of soilmoisture maps with soil maps revealed a direct relation between soil moisturecontents and their changes and soil texture. The spatial and temporal patternobserved in both TB and soil moisture closely followed the pattern of soil texture. Itwas evident that both sandy soils and loamy soils portrayed distinct characteristicsof soil moisture contents and soil moisture drainage. This suggested that remotely-sensed soil moisture and associated temporal changes could be employed to identifysoil type and to estimate soil hydraulic properties. A methodology was developedfor the estimation of pro® le Ksat using a hydrologic model and a GIS. As part ofthe methodology, optimization was carried out to generate soil hydraulic propertiesand hence harmonic-mean Ksat for 5 cm, 30 cm and 60 cm depths. Regression relationswere established between 2-days initial soil moisture change obtainable from remote

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(b)(a)

(c)

Figure 10. Relation between pro® le harmonic-mean Ksat and 2-days initial changes in surfacesoil moisture obtainable from microwave remote sensing. (a) 5 cm depth, y=Õ 1 1́143+101 7́8x, R=0 7́8215. (b) 30 cm depth, y= Õ 5 6́138+84 5́71x, R=0 8́2669.(c) 60 cm depth, y= Õ 4 2́886+75 2́98x, R=0 7́1232.

sensing and pro® le harmonic-mean Ksat for the three depths. Results demonstratedthat temporal changes in surface soil moisture observed from remote sensing can beused to estimate pro® le soil hydraulic properties. The ® ndings have importantimplications to the hydrologic research in that microwave remote sensing can beutilized to generate quantitative soil properties and their spatial distributions overlarge areas.

Acknowledgments

This work was performed in part while Nandish M. Mattikalli held a NationalResearch Council Ð NASA GSFC Post-doctoral Research Associateship. The sup-port was provided by the Science Division of NASA’s O� ce of Mission to PlanetEarth.

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