mapping evapotranspiration using modis .. (1)

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Mapping evapotranspiration using MODIS and MM5 Four-Dimensional

Data Assimilation

Keunchang Jang a, Sinkyu Kang a,, Jeachul Kim a, Chong Bum Lee a, Taehee Kim b, Joon Kim c,Ryuichi Hirata d, Nobuko Saigusa e

a Department of Environmental Science, Kangwon National University, Chuncheon 200-701, Republic of Koreab Department of Groundwater and Geothermal Resources, Korea Institute of Geoscience and Mineral Resources, Daejeon 305-350, Republic of Koreac Department of Atmospheric Science, Yonsei University, Seoul 120-749, Republic of Koread Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, 3-1-3 Kannondal, Tsukuba, 305-8604, Japane National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, 305-8506, Japan

a b s t r a c ta r t i c l e i n f o

Article history:

Received 26 February 2009

Received in revised form 9 November 2009

Accepted 16 November 2009

Keywords:

Evapotranspiration

MODIS

MM5 FDDA

Evapotranspiration (ET), the sum of evaporation from soil and transpiration from vegetation, is of vital

importance in the hydrologic cycle and must be taken into consideration in assessments of the water

resources of any region. The MODerate resolution Imaging Spectroradiometer (MODIS) sensor offers a

promising opportunity for estimating daily ET with a 1 km spatial resolution, but is hampered by frequent

cloud contamination or data gaps from other factors. In this study, 1) a stand-alone ET model was applied

and tested during clear or partial cloudy sky conditions using MODIS-based inputs of land surface and

atmospheric data and 2) meteorological simulations by using Four-Dimensional Data Assimilation (FDDA)

system between MODIS and the 5th Generation Meso-scale Meteorological Model (MM5) was used in

cloudy conditions to facilitate continuous daily ET estimates. The MODIS ET algorithm modified from Mu

et al. (2007) is based on the PenmanMonteith equation and was applied to predict ET at flux measurement

sites. This algorithm considers both the effects of surface energy partitioning processes and environmental

constraints on ET. We devised gap-filling approaches for MODIS aerosol and albedo data that were identified

as bottlenecks to determine retrieval rates of insolation and ET. MODIS-derived input variables (i.e.,meteorological variables and radiation components) for estimating ET showed a good agreement with flux

tower observations at each site. The retrieval rate of MODIS ET doubled at four flux measurement sites after

gap-filling with negligible compensation was undertaken for accuracy. In spite of the high accuracy of

MODIS-derived input variables, MODIS ET showed meaningful errors at the four flux measurement sites.

These errors were mainly associated with errors in the estimated canopy conductance. During clear sky

conditions, MODIS was used to calculate ET, while the MODIS-MM5 FDDA system provided input variables

for the calculation of ET under cloudy sky conditions. The performance of the MODIS-MM5 FDDA system was

evaluated by comparing ET based on MODIS, which showed a good agreement with the MODIS ET for various

land cover types. Our results indicate that MODIS can be applied to monitor the land surface energy budget

and ET with reasonable accuracy and that MODIS-MM5 FDDA has the potential to provide reasonable input

data of ET estimation under cloudy conditions.

2009 Elsevier Inc. All rights reserved.

1. Introduction

Evapotranspiration(ET) is a collective term forall of theprocesses by

which water in the liquid or solid phase at or near the earth's land

surfaces becomes atmospheric water vapor. ET includes the sum of

evaporation of liquid water from surface waters, soil, and surface

vegetation, as well as transpiration from the tissues of plant leaves. Over

the entire land surface of the globe, approximately 62% of the

precipitation returns to the atmosphere with ET from the land's surface

and evaporation from open-water surfaces (Dingman, 1994; Fisher

et al., 2005). ET is not only an important component of hydrological

cycles including precipitation, runoff, streamflow, and soil water

content, but is also a key variable forthe assessment of water resources

in any region. ET also has important effects on climate dynamics and

terrestrial ecosystem productivity because it is closely related to energy

transfer processes (Fisher et al., 2008; Nishida et al., 2003a). Accurate

monitoring of the temporal and spatial distribution of ET is critical to

improving our understanding of energy and hydrologic partitioning

between the land surface and the atmosphere (Boegh et al., 2002;

Cleugh et al., 2007; Mu et al., 2007; Venturini et al., 2008 ). For these

Remote Sensing of Environment 114 (2010) 657673

Corresponding author. Tel.: +82 33 250 8578; fax: +82 33 251 3991.

E-mail address: [email protected] (S. Kang).

0034-4257/$ see front matter 2009 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2009.11.010

Contents lists available at ScienceDirect

Remote Sensing of Environment

j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
mailto:[email protected]://dx.doi.org/10.1016/j.rse.2009.11.010http://www.sciencedirect.com/science/journal/00344257http://www.sciencedirect.com/science/journal/00344257http://dx.doi.org/10.1016/j.rse.2009.11.010mailto:[email protected]


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reasons, many hydro- and agricultural forest-meteorologists have

strived to accurately estimate ET for decades, but it remains difficult

to estimate ET with a high reliability at a large scale area.

Many methods for estimating ET have been developed, and accurate

estimates of ET arebecoming availablethrough the useof ground-based

observations (i.e., the eddy covariance method). However, ground-

based observations cover only a small area at a regional or global scale.

Many studies estimating the spatial and temporal distribution of ET in

largerareashave required a large numberof observation sites because ofthe heterogeneity of landscapes and the wide variation in energy

transfer processes (Nishida et al., 2003a; Wang et al., 2006). However,

this approach is expensiveand labor intensive, so other approaches have

attempted to use satellite remote sensing to estimate ET at large scales

(Nishida et al., 2003a; Shin & An, 2007; Wang et al., 2006). Satellite

remote sensing, especially the MODerate resolution Imaging Spectro-

radiometer (MODIS) sensor, offers promising techniques for estimating

ET with temporally and spatially continuous information over land

surfaces. The MODIS sensor provides a number of biophysical variables

from land surfaces and from the atmosphere, allowing ET to be

estimated. Several recent studies have used various techniques in

applying MODIS products to spatially and temporally continuous

monitoring of ET (Cleugh et al., 2007; Mu et al., 2007; Nishida et al.,

2003b; Venturini et al., 2008; Wang et al., 2006).

Nishidaet al.(2003b) and Wang et al. (2006) proposed methods for

estimating the evaporative fraction (EF), defined as the ratio of ET to

available energy, using land surface temperature (LST) and vegetation

index derived fromMODIS. Cleugh et al. (2007) developed an algorithm

based on the PenmanMonteith equation (PM), the resistance-surface

energy balance and the PenmanMonteith algorithm (RS-PM), to

estimate ET using MODIS products and flux tower measurement data

in two different ecosystems. Mu et al. (2007) proposed a modified

algorithm based on RS-PM (Revised RS-PM)usingboth MODIS products

and meteorological datasets from the Global Modeling and Assimilation

Office (GMAO). This algorithm considers canopy conductance and

environmental constraints (i.e., minimum air temperature, Tmin, and

vapor pressure deficit, VPD) to calculate ET, and added an evaporation

term to the soil. Jang et al. (2009) estimated radiation components and

instantaneous ET using MODIS products and the Revised RS-PMalgorithm using a modification for surface conductance. The PM

equation is complex and data-demanding, so an alternative, the

PriestleyTaylor equation (PT), has been widely applied over the last

three decades. Venturini et al. (2008) proposed a new PT algorithm for

estimating ET using MODIS atmospheric and land products alone.

However, optical and thermal satellite data such as MODIS contain

some limitations, such as cloud contamination, missing data caused by

algorithm problems (Mu et al., 2007; Zhao et al., 2005) and a low

frequencyof measurements compared to that of ground-basedobserva-

tions.Some researchers have used data frommeteorologicalor radiative

transfer models to overcome these problems. For example, Boegh et al.

(2004) reported a methodology for estimating ET combining AVHRR

satellitedata anda high-resolutionweather forecast model (HIRLAM) in

Denmark. The 5th Generation Meso-scaleMeteorological Model (MM5;Grell et al., 1995) is a particularly promising tool for providing the

meteorological variables required as input for ET estimation because of

its ability to assimilate other forms of data with satellite data (e.g.,

MODIS). Satellite data provide the ability to monitor atmospheric infor-

mation from outer space, and have been incorporated into weather

predictions using a data assimilation approach. In previous studies, the

Four-Dimensional Data Assimilation (FDDA) approach was used to

incorporate MODIS data in MM5 (hereafter, MODIS-MM5FDDA), which

caused the enhancement of weather predictions (Xavier et al., 2006;

Yamazaki & Orgaz, 2005). One of the byproducts of the MODIS-MM5

FDDA is the ability to provide missing data (i.e., meteorological data)from satellite observations during cloudy condition, which thusenables

ET to be estimates under cloudy conditions or during any temporal gaps

in the data.

In this study, continuous monitoring of daily ET under all sky

conditions was developed by integrating MODIS data and MODIS-MM5

FDDA. We first tested the reliability of the MODIS-derived biophysical

variables for calculating ET using Eqs. (1)(3) (see Section 2.2.1). The

stand-alone MODIS ET application uses MODIS land surface and

atmospheric products (see Section 2.2.2). Our next objective was then

to develop continuous estimates of daily ET by using MODIS products

which are gap-filled and replaced by meteorological MM5 simulations

during cloudy conditions (see Section 2.2.3). For clear sky conditions,

the stand-alone MODIS ET wasevaluated at fourflux measurementsites

with different biome types in East Asia. The MODIS-MM5 FDDA was

implemented and tested for its improved prediction, which provided

meteorological data to estimate daily ET for cloudy sky conditions. In

this study, the revised RS-PM algorithm (Jang et al., 2009; Mu et al.,

2007) was applied to estimate ET and its sensitivity to input variables

and major sources of error were analyzed and discussed for further

improvement of the algorithm.

2. Materials and methods

2.1. Study sites

Four flux tower sites and one river basin were selected for algorithm

tests and spatial mapping of daily ET, respectively. To evaluate meteo-

rological variables and radiation components derived from the MODIS

and to validate ET retrieved from the MODIS stand-alone algorithm, weused the observed input data and the latent heat flux (E) from four

eddy covariance flux towers in East Asia. The study sites in Korea in-

cluded a cooltemperate deciduous forestin Gwangneung Experimental

Forest(GDK; Kim et al., 2006) and a dry crop farmlandin Haenam(HFK;

Lee et al., 2003). The Japanese study sites were a cool temperate

deciduous forest in Takayama Forest (TKY; Saigusa et al., 2008) and a

Japanese larch forest in Tomakomai (TMK; Hirata et al., 2008). The

location, dominant species, tree age,and maximum LAIof study sites are

described in Table 1.

Tower flux measurements at the GDK and HFK have been con-

ducted since 2000 and 2002, respectively. Air temperature (Ta) and

actual vapor pressure (ea) were measured at a height of 40 m above

the ground at the GDK and 20 m above the ground at HFK using a

CSAT3 sonic anemometer (Campbell Sci., Inc., USA) and a HMP-35Humicap (Vaisala, Finland),respectively. Radiation components at the

GDK were measured at a height of 40 m, while those at the HFK were

Table 1

Description of the study sites.

Site ID Location

(N, E)

Elevation

(m)

Temperaturea

(C)

Precipitationa

(mm)

Dominant species Tree age

(year)

LAIb

(m2 m2)

Study periodc No. of data days

(clear sky days)

References

GDK 37. 45, 127.90 340 11.5 1332 Quercus sp., Carpinus sp. 80200 6 20042892006365 434 (88) Kim et al. (2006)

HFK 34.55, 126.57 13 13.3 1306 Seasonally cultivated crops 1 3 20040012006365 356 (141) Lee et al. (2003)

TKY 36.15, 137.42 1420 6.5 2275 Betula, Quercus sp. 50 4 20020012004365 1096 (67) Saigusa et al. (2008)

TMK 42.74, 141.52 140 6.2 1043 Larix Kaempferi 45 9.2 20020012003365 730 (54) Hirata et al. (2008)

a Annual mean temperature and precipitation from the AsiaFlux website ( http://asiaflux.yonsei.kr/).b Maximum LAI.c

Data period used in this study.

658 K. Jang et al. / Remote Sensing of Environment 114 (2010) 657673
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measured at a height of 15 m above the ground using a Kipp & Zonen

CNR-1 radiometer (Kipp & Zonen, Netherlands). E was measured

using an Li-7500 open-path H2O/CO2 analyzer (Li-Cor, USA; Kim et al.,

2006; Lee et al., 2007a,b).

Flux measurements at the TKY and TMK in Japan have been

established since 1993 and 2000, respectively. For the TKY, Ta and ea(HMP 233, Vasala, Finland), and radiation components (MR-50, EKO,

Japan) were measured at a height of 25 m, while these variables at the

TMK were measured at a height of 40 m above the ground. E wasmeasured using a Li-6262 open- and closed-path H2O/CO2 analyzer(Li-Cor, USA) at the same height (Hirata et al., 2008; Saigusa et al.,

2008). Both sets of measurements were acquired at average intervals

of 30 min (Hirata et al., 2008; Kim et al., 2006; Lee et al., 2007a,b; Lee

et al., 2003; Saigusa et al., 2008).

To extend the spatial scale with the revised RS-PM and to test the

applicability of continuous monitoring of ET using the MODIS-MM5

FDDA system, we selected the Geum river basin that is located in the

mid-western Korea Peninsula. It covers 14,470 km2 andland usetypes

in the basin include forests (61.5%), cropland (31.6%), grassland

(1.4%), and other types (3.2%, i.e., urban, water, and wetland).

2.2. Algorithms

2.2.1. Evapotranspiration

The PM algorithm for computing potential ET has had many suc-

cessful applicationsin hydrologicaland agricultural foreststudies and in

a variety of hydroclimaticregimes (Chenet al., 2005; Cleughet al., 2007;

Federer et al., 1996; Nishida et al., 2003b). The revised RS-PM used in

thisstudy is a modification of the RS-PM algorithm proposed by Cleugh

et al. (2007). Mu et al. (2007) improved the RS-PM algorithm in four

ways: (1) canopy conductance (Cc) is controlled by both daily Tmin and

VPD; (2) the Leaf Area Index (LAI) is expressed as a scalar to expand

fromstomatal conductance(gc) at theleaf level to a canopyconductance

(Cc); (3) replacing the Normalized Difference Vegetation Index (NDVI)

with the EVI (Enhanced Vegetation Index) in calculating the fractions of

vegetation cover; and (4) using a soil evaporation term to account for

areas with sparse canopy cover. Latent heat flux (E; Eq. (1)) was

divided into canopy ET (Eveg; Eq. (2)) and soil evaporation (Esoil;Eq. (3)) to estimate total ET.

E = Eveg + Esoil 1

Eveg =Rn + cpesea = ra + 1 + rS = ra

2

Esoil =Rn;soil + cpesea = ra

+ rtot = ra

RH

100

esea =1003

where (Pa K1) is the slope of the curve relating saturated watervapor pressure (es, Pa) to temperature (K); (Pa K

1) is the psy-

chrometric constant; Rn (W m2) is available energy; (kg m3) is air

density, cp (J kg

1 K

1) is the specific heat capacity of air; ea (Pa) is theactual water vapor pressure; ra (s m

1) is the aerodynamic resistance;

and the surface resistance (rs, s m1) is the effective resistance to

transpiration from the plant canopy. More details on the resistance

terms are summarized in Appendix A.

Stomatal conductance (gc) was calculated using maximum leaf

conductance (CL) with environmental controls of daily minimum air

temperature (Tmin) and vapor pressure deficit (VPD, i.e., esea).Complete details on the scheme for computing stomatal conductance

and values for the environmental control constraints are described in

the Biome Properties Look-Up Table (BPLUT)in the MODIS17 Gross and

NetPrimary Production (GPP/NPP) algorithm (Heinsch et al., 2003). For

each biome type, the BPLUT provides values of maximum leaf

conductance (CL), defined as the mean potential stomatal conductance

per unit leaf area. Our preliminary test on MODIS ET resulted in

considerable underestimation, compared with flux tower ET. In this

study, therefore, we applied a different dataset of maximum leaf

conductance(CL), as suggestedby Federer et al.(1996), for a typical land

cover type (Table 2).

We also added a new multiplicative term (fs) in calculating canopy

conductance (Cc) from stomatal conductance (gc) and LAI (Eq. (4)).

The fs variable is a shelter factor that accounts for the fact that some

leaves are sheltered from the sun and wind and thus transpire at

lower rates (Dingman, 1994). Based on other studies, fs decreasesmonotonically from 1 to 0.5 with no canopy (LAI=0) and closed

canopy (LAI= 3; Allen et al., 1989; Carlson, 1991; Eq. (5)). We

calculate fs for LAI changes as shown in Eq. (5):

1

rs= Cc = fs LAI gc 4

fs =fsopen +

LAILAImin fsclosefsopenLAImaxLAImin

0 LAIb 3

fsclose LAI 3

8>: 5

where, fs_open and fs_close are shelter factors for sparse and dense

vegetation, which take on values of 1 and0.5, respectively; LAI, LAImin,

and LAImax are leaf area indices for current, leafless (no canopy), anddense (closed canopy) vegetation status.

In this study, Eqs. (1)(3) were utilized to estimate instantaneous

ET at MODIS overpass time by using inputs from MODIS data for clear

or partial cloudy condition. The equations were also used for daily ET

estimation on cloudy days by using meteorological input data from

MODIS-MM5 FDDA simulation.

2.2.2. MODIS-derived instantaneous and daily evapotranspiration

The instantaneous ET (IET, W m2) during the satellite overpass

was estimated by Eqs. (1)(3) with MODIS-derived input variables.

The procedure of IET estimation was described in Fig. 1 and equations

associated with the process were summarized in Appendices AC.

Input meteorological variables for calculation of IET were either

extracted directly or estimated from MODIS atmospheric and landproducts. Especially, MODIS-derived net radiation, as a key variable of

ET estimation was explained in Section 2.2.4.

Many hydrological applications and models require ET at a daily

level (mm day1). Nishida et al. (2003a) reported evaporation

fraction (EF) as an index for ET, which was defined as a ratio of

latent heat flux (ET, W m2) to available energy (generally equal to

INR, W m2; Shuttleworth et al., 1989). Other previous studies

showed that diurnal patterns of the EF were nearly constant during

the daytime (Nishida et al., 2003a;Shuttleworth et al., 1989). Daytime

ET can, therefore, be estimated using the instantaneous EF concept

when daytime average net radiation is known. In this study, we

further assumed that nighttime ET is negligible and, hence, daytime

ET is nearly equal to daily ET.

Bisht et al. (2005) proposed a method to expand instantaneous netradiation (Rn or INR) to daily average net radiation (DANR) for clear sky

Table 2

Typical values of maximum leafconductance (CL) forthebiome types used in this study

(Federer et al., 1996).

Land cover CL (m s1)

Conifer forest 0.0053

Broadleaf forest 0.0053

Savanna/shrub 0.0053

Grassland 0.0080

Tundra/nonforest wetland 0.0066

Desert 0.0050

Typical crop 0.0011

659K. Jang et al. / Remote Sensing of Environment 114 (2010) 657673


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conditions based on the sinusoidal model of Lagouarde and Brunet

(1983). In this study, we employed a sinusoidal model for estimating

daily ET, which requires time information for satellite overpass (toverpass),

sunrise (trise), and sunset (tset).Time information for trise and tset can be

obtained by using a function of latitude and solar declination angle

(Dingman, 1994). To expand the model to obtain daily ET, DANR is first

multiplied with daytime length (Dlength, i.e., tsettrise) to derive dailytotal Rn. Then, daily ET was estimated by multiplying the instantaneous

EF by the daily total Rn. Thecalculation methods forthe sinusoidal model

and daily ETcanbe found inAppendixA (seealso Bisht et al., 2005 forthe

sinusoidal model), and each parameter for the sinusoidal model was

calculated using the methods suggested by Dingman (1994).

2.2.3. Continuous monitoring of daily ET

For clear or partial cloudy days, daily ET was estimated with input

variables derived from MODIS atmospheric and land products as

described above (Fig. 1). For cloudy days, the MODIS-derived meteo-

rologicalinput data were not provided andhence, replaced with those

from MODIS-MM5 FDDA predictions to estimate cloudy-day daily ET

(Fig. 2). Detailed information on MODIS-MM5 FDDA can be found in

Section 2.4.

Fig. 2 shows a schematic diagram for continuous monitoring of ET

using either stand-alone MODIS (hereafter, MODIS ET) or the

combined use of both MODIS land products and MODIS-MM5 FDDA

(hereafter, MODIS-MM5 ET). The operation of this system isdetermined by sky conditions derived from the clear pixel number

of MODIS07 (see Section 2.3.1). Under clear and partially clear sky

conditions (clear pixel5), MODIS was used to calculate MODISinstantaneous anddaily ET (Fig. 1). Forthe cloudy skycondition (clear

pixelb5), MODIS-MM5 ET was calculated using meteorological

datasets (i.e., air temperature (Ta), humidity (RH), pressure (P), and

radiation components (Rs, Rl, and Rn)) derived from the MODIS-MM5

FDDA and MODIS land products (i.e., vegetation indices and albedo).

In both cases, the Eqs. (1)(3) were utilized with different input

variables. The overall processing stream for estimating MODIS-MM5

ET is identical to those for MODIS ET, but meteorological variables

produced via the MODIS-MM5 FDDA were used for continuous

estimation of ET under cloudy conditions or for any missing data

period. Subsequently, continuous monitoring of daily ET was

implemented by combining both MODIS ET and MODIS-MM5 ET for

clear or partially clear days and for cloudy days, respectively.

2.2.4. Radiation components

Net radiation (Rn, W m2) is a key variable for understanding energy

budgets, climate monitoring, weather prediction, and agricultural meteo-

rology, and is defined as the difference between downward and upward

radiation components at the land's surface. It can be expressed as:

Rn = Rs Rs + Rl Rl 5

Bisht et al. (2005) and Ryu et al. (2008) proposed a method for

estimating Rn derived entirely from MODIS. In this study, we employed

the methodology for estimating Rn suggested by Ryu et al. (2008). Rs

(equalto insolation) is a vital component of land surface energybudgets

and is used as a key meteorological input variable, forcing ET and

photosynthesis in many ecosystem and hydrological models (Hwang

et al., 2008; Kang et al., 2002; Landsberg & Waring, 1997; Running &

Coughlan, 1988). We used the parameterization scheme for Rs

developed by Bird and Hulstrom (Clear Sky Model; 1981) to consider

both diffuse and direct insolation. The Clear Sky Model has been

evaluated in several studies and produced accurate results for

estimating insolation (Annear & Wells, 2007; Chen et al., 2007; Ryu

et al., 2008). Recently, the Clear Sky Model was reevaluated and wasdetermined to be the most accurate clear sky insolation model among

five alternative insolation models incorporated into water quality

models (Annear & Wells, 2007). Rs can be described as the proportion

of Rs that is dependent on the albedo () or shortwave reflectancefrom the ecosystem surface. The albedo () data can be derived fromMODIS43 BRDF products. Prata (1996) proposed a scheme for

estimating Rl for clear sky days, which outperforms other widely

used formulas. Bisht et al. (2005) and Ryu et al. (2008) employed this

scheme in their studies, and reported a high accuracy with ground

observations. Rl can be calculated using Prata's (1996) scheme based

on the SteffanBolzmann equation, and we employed Liang's (2004)

scheme to estimate the surface emissivity using MODIS11 emissivity

data. More detailed methods for calculating radiation components,

albedo, and emissivity are described in Appendix B.

Fig. 1. Processing flow for estimating MODIS-based radiation components and evapotranspiration.



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2.3. MODIS data processing

MODIS sensors were launched on board the National Aerodynamics

and Space Administration (NASA) Earth Observing System (EOS) Terra

and Aqua satellites on December 1999 and May 2002, respectively. The

wide spectral range (mainly 36 bands), high spatial and temporal reso-

lution(250,500, and1000 m andmore thantwiceper day, respectively)

of MODIS enables MODIS to observe the earth's atmosphere and land

surface, and to monitor their changes continuously (Bisht et al., 2005;Masuoka et al., 1998; Ryu et al., 2008; Seemann et al., 2003). In this

study, we used the Aqua-MODIS products to estimate ET ( Fig. 1).

2.3.1. Atmospheric products

The MODIS07 atmospheric profile products have a spatial resolution

of 55 km2 and provide several instantaneous variables, of which

latitude, longitude, air and dew-point temperature, total ozone burden,

surface pressure, total precipitable water vapor, and solar zenith angle

(Seemann et al. 2006) were used in this study. Air and dew-point

temperatures were extracted from the lowest valid layer among 20

vertical atmospheric pressure levels and were used to calculate satu-

rated vapor pressure (es) and actual vapor pressure (ea), respectively

(see Appendix B).

The quality assurance (QA) field provides information for the cloudmask, which indicates how many of the 1 1 km2 sub-pixels have clear

sky conditions within a MODIS07 pixel resolution of 5 5 km2. In other

words, if the number of 11 km2 clear pixels is equal to 25, the sky

condition is perfectly clear within a 55 km2 pixel resolution. If the

number of clear pixel is less than 5, the dataset was not produced.

Considering the sky conditions(i.e.,perfect clear, partial clear, or cloudy

sky), we tested the fifth byte of the QA Scientific Data Set (SDS) that is

assigned each 10 byte array and extracted the clear pixel for the sky

conditions.

The MODIS04 aerosol products (10 10 km) provide aerosol

optical depth (AOD) at three microwave lengths (i.e., 0.47, 0.55, and

0.67 m). Instantaneous AOD measurements were used as inputvariables to estimate Rs but the retrieval rate is lower than for

MODIS07 variables, which results in a low retrieval rate for Rs and,

ultimately, forET. Therefore, we increasedthe retrievalratesof MODIS

Rs by gap-filling of themissing aerosol data used byJang et al. (2009).

For the missing aerosol data, we simply used the monthly mean

aerosol value of each pixel from 2004 to 2006.

2.3.2. Land products

The MODIS11A1 products produced by land surface generalized

split-window algorithm contain land surface temperature (LST) and

emissivity for bands 31 (10.7812.38 m) and 32 (11.7012.27 m)with a spatial resolution of 11 km2, both of which were used as

input variables for calculating Rl.

The MODIS13A2 products contain 16-day composite vegetation

indices such as NDVI and EVI (Huete et al., 2002). The vegetation

indices were generally reliable using the 16-day composite method,

but sometimes showed unlikely fluctuations during the early

spring or during the summer rainy season in East Asia. For this

reason, we applied the smoothing technique suggested by Kim et al.

(2008) to reconstruct smoothed seasonal changes in vegetation

indices.

The MODIS43B3 products provide information on 16-day albedo at

various bands. We used the 10th band of the black and white sky

albedos at 1 km resolution to calculate both downward (Rs) and

upward shortwave radiation (Rs). However, MODIS43B3 albedocontains some cloud-contaminated or missing data that was identified

as a majorfactor contributing to thelow retrieval rate of insolation data

and ultimately those of the ET and MODIS04 aerosol products. There-

fore, we reconstitutedcontinuous seasonal changes in albedo by using a

spatial and temporal gap-filling technique which considered the land

cover type, as suggested by Kang et al. (2005)and Jang et al.(2009). The

process has two steps (see Fig. 2 in Kang et al., 2005) including spatial

and temporal interpolation procedures. First, if albedo datafor any pixel

was unreliable, as identified by quality control (QC) flags or missing

values, a fill value for that pixel was derived by spatially interpolating

surrounding cloud-free pixel values having the same land cover type

within an overlying 55 pixel window (25 km2). Second, if there was

no cloud-free pixel within the specified window, a temporal interpo-

lation was performed using the following simple assumption: when the

Fig. 2. A schematic diagram for continuous estimation of daily ET using MODIS land products and MODIS-MM5 FDDA meteorological data.



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unreliable or missing pixel from a previous 16-day period was cloud-

free, thecurrent pixelvalue assumes thevalueof thepreviouscloud-free

pixel.

2.3.3. Sensitivity analysis and error statistics

In this study, we tested the ET model sensitivity to key MODIS

input variables such as Ta, VPD, LAI, and Rn. To evaluate the sensitivity,

VPD, LAI, and Rn were varied from 20 to +20% with respect to

MODIS-derived input variables, but Ta was changed from 2 C to+2 C. The percent change from MODIS-derived ET was calculated toevaluate the model sensitivity.

Mean Error (ME) and Root Mean Square Error (RMSE) were

computed to evaluate the reliability of temperature, VPD, radiation

components, and ET. These error statistics were used to confirm the

bias for model performance and model accuracy in this study. ME and

RMSE were computed by the following equations:

ME =1

n

n

i = 1xsxo

RMSE =1

n

n

i = 1xsxo

2 1=2

where n is the number of samples; and xs and xo are the satellite-

based and ground observation-based values, respectively.

2.4. MODIS-MM5 FDDA

2.4.1. Domains of MM5 simulation

MM5 Version 3 Release 7 (Grell et al., 1995) was developed and

maintained by Pennsylvania State University (PSU) and the National

Center for Atmospheric Research (NCAR). In this study, MM5

simulations were conducted with two nested domains, and had grid

resolutions of 30 and 10 km, respectively. Fig. 3 shows the model

domain setup. The first nested domain (D1), with a spatial resolution

of 30 km, comprises the eastern part of Asia, including China and

Japan. The second domain (D2) covers the southern Korean Peninsulaand part of the surrounding seas. The model employed 35 vertical

layers. The lowest computational layer was approximately 23.6 m

above ground level (AGL) and the top layer was at a height of 50 hPa.

The initial field and boundary conditions were extracted from

Regional Data Assimilation and Prediction System (RDAPS) data

(Lee et al., 2007a,b), which is originally based on the MM5 meso-scale

model developed by PSU/NCAR. The RDAPS is the operational model

in the Korea Meteorological Administration (KMA) and was produced

from the MM5 model simulation with horizontal resolutions of 30 km

(191 171 grids) and 33 levels of vertical terrain-influenced coordi-

nates, centered at 38 N, 126 E in the East Asia region. The other

major components used in this model include mixed-phase micro-

physics (Reisner et al., 1998), simple shortwave radiation physics

with cloud interactive radiation (Dudhia, 1989), the RRTM (RapidRadiative Transfer Model) longwave radiation (Mlawer et al., 1997),

the Grell convective scheme (Grell et al., 1995) and the MRF planetary

boundary layer (PBL) scheme (Hong & Pan, 1996).

2.4.2. Implementation of MM5 FDDA

During the numerical simulations, large errors can accumulate due

to the uncertainties in the model. To help control the growth of errors,

MM5 assimilates observations both at the initial time and continuously

throughout the integration period. This technique, known as the Four-

Dimensional Data Assimilation (FDDA) or nudging (Stauffer &

Seaman, 1994), uses an explicit dynamic predictive model combined

with observations to follow the evolving atmospheric fluid dynamics.

FDDA is a suitable technique for incorporating observations or satellite

data into a forecast model such as MM5. The model's prognostic

equationscombine current andpast datato provide temporal continuity

and dynamic coupling among the various fields analyzed.

We performed data assimilation with selected observational and

MODIS data. The data assimilation for MM5 has two different nudging

procedures: analysis vs.observational nudging. In this study, we applied

two nudging process, simultaneously. Observations from ground

weather stations and a few radiosonde data in the East Asia region

were used for analysis nudging, while MODIS atmospheric profile ofair

temperature was utilized for observational nudging, respectively. Gridmaps of the 20 vertical layers of temperature profile of MODIS07 were

prepared for the observational nudging. Each grid map has information

of air temperature of each clear sky pixel at the corresponding pressure

level.

We attempted numerical experiments to investigate the impact of

alternative nudging methods (i.e. without and with MODIS data) on

MM5 FDDA simulations: one adopted only analysis nudging with a few

radiosonde and meteorological data, but the other utilized the analysis

nudging together with the observational nudging using the vertical

profiles of MODIS temperature, respectively. The latter case is MODIS-

MM5 FDDA as indicated in this study and the former is conceived as

conventional MM5 FDDA. The numerical experiments were utilized to

evaluate the performance of the MODIS-MM5 FDDA by comparison

with the conventional MM5-FDDA simulation. MODIS observations of

profiler temperatures were adjusted slightly for the 10 km domain. In

this process, MODIS provided 20 vertical layers of temperature data for

clear sky days for the MM5 FDDA. The MODIS-MM5 FDDA system

provides various meteorological dataset, including air temperature,

humidity, pressure, and radiation components that are key input

variables for calculating ET under cloudy conditions.

3. Results

3.1. Meteorological variables

The MODIS-derived air temperature showed a good agreement

with the four flux tower measurement sites for clear sky conditions

(Table 3). For the GDK site, the RMSE for 20042006 were between

2.8 C and 3.6 C, and correlation coefficients (r) were from 0.96 to0.97. HFK also showed a good agreement with a RMSE of 2.5 C and

rvalues of 0.96 for 3 years (20042006). For the TKY, RMSE and rfor

20022004 were 2.4 C and 0.95, and those for the TMK were 2.8 C

and 0.94, respectively. The magnitudes of the errors were generally

similar to or smaller than those reported in previous studies. Prihodko

and Goward (1997) estimated air temperature using the relationship

of surface temperature and vegetation index from AVHRR over

northeastern Kansas. They reported a RMSE of 2.9 C. Houborg and

Soegaard (2004) showed RMSE of 2.5 C using Terra-MODIS over an

agricultural area in Denmark, and Bisht et al. (2005) reported 5.0 C of

RMSE over the Southern Great Plains, USA.

Actual vapor pressure (ea), estimated using MODIS07 dew-point

temperatures, showed a reasonable agreement with the field

measurements at four sites. The RMSE at the GDK, HFK, TKY, andTMK were 234, 286, 354, and 246 Pa, respectively. These results were

generallysimilar to or smaller than those reported in Ryu et al. (2008)

for two flux tower sites in Korea (Terra, 400 Pa and Aqua, 320 Pa).

Vapor pressure deficit (VPD), defined as the difference between

saturated vapor pressure and actual vapor pressure, also showed a

good agreement with the field measurements. However, VPD was

generally underestimated at the GDK, HFK, and TMK sites, but

overestimated at the TKY. More details for the error analysis of VPD

are presented in Table 3.

3.2. Radiation components

Forfour flux tower sites and Geum river basin, we applied a simple

gap-filling approach (Jang et al., 2009) for MODIS aerosol and albedo



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data that were identified as a bottleneck to determine retrieval rates

ofRs and Rn for clear sky days. Jang et al. (2009) evaluated the gap-

filled Rs using ground-based measurement data from 22 stations of

the Korea National Weather Stations (NWS), which resulted in a

considerable increase in the retrieval rate of MODIS Rs. In this study,

even after gap-filling for MODIS aerosol and albedo data, the retrieval

rates of MODIS Rs for the flux tower sites remained at approximately

20% or so for Aqua-MODIS. By including partially clear sky conditions

with the gap-filling techniques for aerosol and albedo, the retrieval

rate increased from 16 to 44% for the study areas.

All statistics information for the four gap-filled radiation compo-

nents and net radiation are summarized in Table 3. Rs showed a good

agreement (a coefficient of determination of over 0.82) at all sites, butwas generally underestimated. ME and RMSE were 29.3 and69.3 W m2 at the GDK, 38.1 and 54.1 W m2 at the HFK, 46.7and 74.4 W m2 at the TKY, and 12.0 and 85.6 W m2 at the TMK,respectively. Rs was slightly underestimated at the TKY and TMK sites

with a ME (RMSE) of27.3 (34.8) W m2 and10.6 (19.2) W m2,

respectively. Rl and Rl were underestimated at both TKY and TMK

sites. TMK showed a better agreement (ME, 25.5 and8.6 W m2)with the field measurements than TKY (37.3 and35.7 W m2).

Rn showed a reasonable accuracy with the field measurements

with RMSE (ME) of 90.5 (38.6), 44.1 (+32.1), 61.2 (29.1), and76.4 (16.6) W m2 at the GDK, HFK, TKY, and TMK, respectively.MODIS Rn was generally underestimated at the GDK, TKY, and TMK,

but overestimated at the HFK. The errors found in this study were

comparable with those reported in other studies from Terra-MODIS,

with a RMSE of 74 W m2 (Bisht et al., 2005) and from Terra and

Aqua-MODIS with a RMSE of 4765 W m2 (Ryu et al., 2008).

3.3. MODIS instantaneous and daily ET

In comparison with flux tower measurements, MODIS-derived

clear sky instantaneous ET resulted in an overestimation at the GDK

(ME=+41 W m2) but an underestimation at the HFK (7 W m2),TKY (52 W m2), and TMK (44 W m2) sites (a, b, c, and d in

Fig. 3. Domains used inthe MM5 model. The circles () andtriangles() indicatethe upper-airstations and NationalWeather Stations(NWS), respectively. (D1)and (D2)show the

30 km and 10 km domains of MM5 for predictive simulations.

Table 3

Means (a) and error statistics(b) forMODIS-derived meteorological variablesand radiationcomponentson clear sky daysat fourflux measurement sites. Here, Ta is air temperature;

ea, vapor pressure; VPD, vapor pressure deficit; Rs and Rs, downward and upward shortwave radiation; Rl and Rl, downward and upward longwave radiation; and Rn, net

radiation.

Site Ta (C) ea (Pa) VPD (Pa) Rs (W m2) Rs (W m

2) Rl (W m2) Rl (W m

2) Rn (W m2)

(a) Means offlux tower measurements with MODIS-derived variables in parenthesis

GDK 11.0

(10.0)

664.3

(686.8)

946.9

(711.2)

681.9

(652.6)

(74.4)

(285.5)

(551.4)

534.8

(488.5)

HFK 16.0

(15.6)

877.8

(970.5)

1129.6

(920.8)

766.4

(728.3)

(118.2)

(312.9)

(417.4)

474.9

(507.0)

TKY 12.5

(11.7)

910.7

(687.6)

633.2

(821.1)

782.9

(736.4)

111.0

(83.7)

319.8

(282.5)

417.2

(391.6)

580.3

(550.7)

TMK 11.5

(10.0)

859.6

(928.0)

545.8

(552.8)

696.7

(684.7)

91.8

(81.2)

318.0

(292.5)

397.8

(389.2)

551.7

(535.1)

(b) Mean error (ME) with root mean square error (RMSE) in parenthesis

GDK 1.0(3.5)

+22.5

(233.5)

222.3(269.1)

29.3(69.3)

()

()

()

38.6

(90.5)

HFK 0.4(2.5)

+92.3

(258.8)

205.8(387.7)

38.1(54.1)

()

()

()

+32.1

(44.1)

TKY 0.7(2.4)

223.1(347.9)

+188.0

(188.0)

46.7(74.4)

27.3(34.8)

37.3(45.3)

35.7(30.9)

29.1(61.2)

TMK 1.5(2.8)

148.3(243.3)

+7.0

(293.4)

12.0(85.6)

10.6(19.2)

25.5(28.9)

8.6(14.8)

16.6(76.4)



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Fig. 4). HFK showed more scattered relationships (r=0.60 and

RMSE= 84 W m2) than GDK (0.76 and 75 W m2), TKY (0.66 and

119 W m2), and TMK (0.74 and 91 W m2), respectively (Table 4).

Uncertainty in MODIS-derived meteorological data (Table 3) affects

errors in the stand-alone MODIS-derived instantaneous ET. Hence, the

tower observed meteorology (i.e. air temperature, vapor pressure, and

net radiation) were utilized together with MODIS vegetation indices to

test reliability of the ET algorithm suggested in this study. Except for

GDK site(ME=+64 W m2), the useof tower meteorology resulted in

better agreements with the flux tower ET at HFK (+2 W m2), TKY

(10W m2), and TMK (0.3 W m2), respectively (Table 4). Thisindicates that our ET algorithm estimates more reliable ET with more

accurate meteorological data but still contains other source of

uncertainty in addition to the input meteorology.

Fig. 4. Comparison of MODIS-derived instantaneous ET (W m2) and flux tower measurements. Left images show instantaneous ET for clear sky conditions (clear pixel=25) and

right images show instantaneous ET for the clear and partial clear sky conditions (clear pixel5) at the GDK (a, e), HFK (b, f), TKY (c, g), and TMK (d, h).



9/17

We also compared MODIS instantaneous ET with flux measure-

ments for all sky conditions, including both perfect and partial clear

sky conditions (MODIS07 clear pixel N=5; e, f, g, and h in Fig. 4).

Including partial clear sky conditions did not significantly reduce the

accuracies of MODIS ET at the four sites. The ME and RMSE at the GDK

and TKY and ME at the HFK decreased rather than increased for data

with clear sky conditions, while the ME and RMSE at the TMK and

RMSE at the HFK increased (Table 4). By including partial clear sky

conditions, the retrieval rates of MODIS ET increased from 21 to 35% at

the GDK,from 16to 27% attheHFK,from 8 to17%at the TKY,andfrom

10 to 19% at the TMK, respectively, with negligible compensations in

accuracy.Instantaneous ET (W m2) was converted to daily ET (mm day1)

using our scaling method in Appendix A.3. Comparisons between

MODIS daily ET and flux tower measurements showed a better agree-

ment (Fig. 5) thanthose for theMODIS instantaneous ET(Fig. 4). MODIS

daily ET resulted in an overall overestimation at the GDK with ME

of +0.48 mm day1, but an underestimation at the HFK (0.19 mmday1), TKY (0.53 mm day1), and TMK (0.56 mm day1). Aswell, a better linearity was observed for daily ET with the flux tower

data for the GDK (r=0.90, RMSE=0.79 mm day1), HFK (0.81 and

Table 4

Errorstatistics (MEwith RMSEin parenthesis)for Aqua-MODISinstantaneousET (W m2)

anddailyET (mmday1) atflux sites.InstantaneousET analyzed forclear skyand partially

clear sky conditions, and daily ET analyzed for clear sky conditions.

Site Instantaneous ET

for clear skya

(W m2)

Instantaneous ET

for clear skyb

(W m2)

Instantaneous ET for

partially clear skyb

(W m2)

Daily ETb

(mm day2)

GD K +63.6 (100.0) +41.0 ( 75.4) +30.8 ( 73.3) +0.06 ( 0.19)

HFK +2.4 (79.0) 7.1 ( 84.1) +2.8 ( 87.4) 0.19 (0.79)TKY 10.2 (109.4) 52.1 (119.2) 32.1 (99.3) 0.53 (0.88)TMK 0.3 (77.4) 44.7 (91.6) 59.1 (132.6) 0.56 (1.01)a ET estimated using flux tower meteorological data and MODIS vegetation indices

product with the revised RS-PM algorithm.b ET estimated using MODIS atmospheric and land products with the revised RS-PM

algorithm.

Fig. 5. Comparison of MODIS dailyET (mm day1) estimatedusing a sinusoidalmodel andflux towermeasurements forthe clear skycondition atthe GDK(a),HFK (b), TKY(c), and

TMK (d).



10/17

0.79 mm day1), TKY (0.84 and 0.88 mm day1), and TMK (0.81 and

1.01 mm day1) than those of instantaneous ET.

3.4. MODIS-MM5 ET

Geographical dependence on model predictions is instructive, and

thus was investigated through calculating and mapping the main

statistics. Fig. 6 shows the spatial distribution of correlation

coeffi

cients (r) for air temperature (Temp, equal to Ta) and specifi

chumidity (Q2) at a 2 m height and wind speed (WS) at a 10 m height

for numerical experiments before and after MODIS-MM5 FDDA.

Correlation coefficients (r) after MODIS-MM5 FDDA (a, c, and e in

Fig. 6) were higher than those from the prediction of MM5 alone (b, d,

and f in Fig. 6). These results indicated that the FDDA system can

simulate the temporal and spatial variability for near-surface with

improved accuracy.

The improved meteorological information produced by MODIS-

MM5 FDDA was combined with MODIS land product information to

calculate MODIS-MM5 daily ET (Fig. 3). The MODIS-MM5 ET was used

for cloudy days to provide a scheme for continuous monitoring of

daily ET for all sky conditions. First of all, we compared MODIS-based

daily ET (i.e., MODIS ET) with FDDA-based daily ET (i.e., MODIS-MM5

ET) to evaluate the predictive abilities of these two techniques. Fig. 7

shows time series and scatter plots between MODIS ET and MODIS-

MM5 ET for different land cover types (i.e., forest, cropland, and

grassland) over the Geum river basin on dates when the MODIS ET

was retrieved. The time series pattern of MODIS-MM5 daily ET

showed a good agreement with the pattern of MODIS daily ET for all

land cover types. This suggests that FDDA-based products can be

merged with MODIS-based daily ET and can be used to make up for

weak points due to missing MODIS data during cloudy days. We used

this technique, and produced accumulated ET over the Geum river

basin in 2006 (Fig. 8). The average of annual accumulated ET over the

Geum river basin was approximately 750 mm y1, and showed major

spatial variations among land cover types. The accumulated ET

(average 1000 mm) for cropland was generally higher than other

land cover types, including forests and grasslands. These trends could

be due to the effect for the relatively high value of maximum leafconductance (CL=11.0 mm s

1) for croplands.

3.5. Sensitivity analysis

We also conducted a model sensitivity analysis on atmospheric

and land surface data derived from MODIS. Fig. 9 shows the model

sensitivity (relativepercent change) to the key input variables (i.e., Ta,

VPD, LAI, and Rn) on the estimation of ET using the Revised RS-PM

algorithm. A relative percent change in ET of up to 12% was caused

by changing Rn with 20% at each site. Changes in ET of 911% were

found for 20% changes in LAI. Changes of Rn and LAI showed a linear

relationship with ET percent changes as expected from the algorithm.

VPD and Ta, however, showed nonlinear relationships with ET. The

sensitivity of ET to VPD was relatively low because of positive effectsof VPD on forced evaporation and negative effects on stomatal

opening. Our results based on the Revised RS-PM algorithm indicate

that for the deciduous sites (the GDK and TKY), the positive effect was

more than offset by higher negative impact on stomatal opening. Tawas associated with many terms of the ET process, including Rl, ,VPD, and stomatal and aerodynamic conductance. For the two

deciduous sites, the positive effects of Ta on VPD and stomatal

conductance were offset by adverse effects on net radiation, which

resulted in a lower sensitivity than at the HFK and TMK sites.

4. Discussion

In this study, we have developed a stand-alone algorithm to

estimate instantaneous and daily ET using only MODIS products. The

stand-alone algorithm is based on the Revised RS-PM proposed by Mu

et al. (2007) but does not require any input variables other than

MODIS products and estimated ET with reasonable accuracy. Also, the

parameterization of maximum leaf conductance (CL) and the addition

of the multiplicative term (fs) in the calculation for canopy con-

ductance (Eq. (4)) implicitly accounts for the sheltering effect caused

by phenological change, and reproduced the improvement of MODIS

instantaneous ET. Various meteorological variables (i.e., Ta, ea, and

VPD) derived by MODIS products showed a good correlation withground-based observations (Table 3) for clear sky days. Improve-

ments in MODIS products for estimating ET increased the retrieval

rate (%). In particular, enhancement of MODIS04 aerosol and

MODIS43 albedo products using the gap-filling method led to

increases in the retrieval rate for Rs, Rn, and ET and resulted in a

good agreement with the flux measurements.

The framework presented here that is used to estimate ET is

applicable for clear sky conditions only. For continuous monitoring of

land surface radiation components and ET, however, the average

retrieval rate of 20% for clear sky conditions still requires further

improvements. MODIS-derived ET for partially clear sky conditions

showed similar errors to those for clear sky conditions, while retrieval

rates nearly doubled (Fig. 4).

In this study, the MODIS-MM5 FDDA was implemented to provide

meteorological data for cloudy conditions for continuous monitoring

using satellite data. Other recent efforts for satellite monitoring of

land surface radiation components and ET during all sky conditions

are worthy of note. Tang et al. (2006) suggested a direct method for

estimating shortwaveradiation (Rs) using multispectral narrowband

data of MODIS for all sky conditions. They reported that RMSE to Rs is

less than 20 W m2 for clear sky conditions and 35 W m2 for cloudy

conditions. Liang et al. (2006) reported the look-up table approach to

estimate incident photosynthetically active radiation (PAR) using

MODIS. These approaches were applied using top-of-atmosphere

(TOA) radiance, which contains information on both the atmosphere

and surface. Comparison of their PAR results with ground data at

seven FLUXNET sites yielded the average relative error (%), which

ranges from 4.1 to 21.9%. Also, Jutla and Islam (2007) presented a

method for estimating MODIS-based ET using the PriestleyTaylorequation under all sky conditions, and Jutla and Islam (2008)

suggested that MODIS06 cloud fraction and cloud optical depth

products could be applied to estimate instantaneous shortwave

radiation (Rs) for all sky conditions.

In spite of the high accuracy of the input variables for estimating ET

(Table 3), the comparison with flux measurement ET showed

meaningful errors in MODIS-derived ET (Table 4). The causes of these

errors might be related to operational limitationsof MODIS (i.e., a lower

temporal resolution, such as 16-day MODIS products or a coarse spatial

resolution over heterogeneous land cover and/or complex terrain), as

well as the algorithm or algorithm parameters for estimating ET. Mu

et al. (2007) discussed the limitations of the parameters in their

algorithm, including: (1) effects caused by the aerodynamic resistance

from plant transpiration and the aerodynamic resistance from soilevaporation and (2) the refinement of surface resistance. In this study,

we tested the effect of the canopy conductance against flux measured

canopy conductance, which is equal to the inverse of surface resistance.

Canopy conductance is one of main parameters used to estimate ET

and is controlled by VI and environmental constraints (i.e., Tmin and

VPD) in this study. Fig. 10 shows the comparison of differences in

instantaneous ET and canopy conductance in the MODIS and flux

measurements at the GDK, HFK, TKY, and TMK. The coefficients of

determination (r2) were 0.74 at the GDK, 0.71 at the HFK, 0.89 at the

TKY, and 0.66 at the TMK. This result suggests that canopy conductance

significantly affected the error of the instantaneous ET. In this study,

rather than using the MODIS15 leaf area index (LAI) product, we used

LAI derivedfrom the 16-day MODIS13vegetation index product (NDVI)

proposed by Fisheret al. (2008) to obtain canopy conductance.We used



11/17

Fig. 6. Each map shows distributions of correlation coefficients (r) between weather stations and MM5. The size of each circle () is scaled from the minimum (0.15) to the

maximum values (1) for each map. (a), (c), and (e) showed the result of MM5 only, and others showed the result of MODIS-MM5 FDDA.



12/17

Fig. 7. Comparisons of MODIS-based and FDDA-based daily ET for land cover types over the Geum river basin. The rectangles () and dashed lines () in left images indicate

MODIS-based and FDDA-based daily ET, respectively.

Fig. 8. Annual accumulated ET (mm)map derived usingthe mergence technique betweenMODIS and FDDA datasets under cloudy sky conditions over the Geumriver basinin 2006.



13/17

Fig. 9. The model sensitivity (relative percent change, %) to the change rate of the key input variables on the estimation of ET at the GDK (a), HFK (b), TKY (c), and TMK (d). Ta, VPD,

LAI, and Rn indicate air temperature, vapor pressure deficit, lead area index, and net radiation, respectively.

Fig. 10. Comparison of the difference between measured and MODIS instantaneous ET (W m2) and the difference between measured and MODIS canopy conductance (CC) at the

GDK (a), HFK (b), TKY (c), and TMK (d).



14/17

this product becausethe MODIS15 LAI product produced by the MODIS

Standard algorithm tends to be higher than ground observations

(Cleugh et al., 2007; Heinsch et al., 2006; Mu et al., 2007; Wang et al.,

2004). Canopy conductance is influenced by environmental constraints

(i.e., Tmin and VPD) and phenological changes. For the relatively long

(i.e., 16-day) periodof this study, phenologicalchanges at thebeginning

and end ofthe seasonsmaybe difficultto detectfor biome type.Evenso,

we found that there was a bias, as the difference between canopy

conductance valuescannot be explainedby thebias in instantaneousET.Hence, this analysis indicates that a more extensive parameterization of

canopy conductance and evaluation of aerodynamic resistance for the

vegetation and soil components are required to improve the prediction

ability of the revised RM-PM algorithm.

The MODIS07 air temperature profiles (50 to 1000 hPa) were ex-

tractedas grid cells forthe FDDA. These cells were then interpolatedinto

35 sigma pressure levelsof MM5. Thedata assimilation betweenMODIS

and MM5 showed improvement for predicting simulations of MM5

(Fig. 6). In particular, air temperature and humidity data used in the

Revised RS-PM showed an enhancement for spatial and temporal

distributions. Theimproved data setvia theMODIS-MM5 FDDA used for

estimating ET at scales greater than the basin scale and the FDDA scale

was suggested as a solution for obtaining data under cloudy conditions.

We have produced the daily ET map, which ranges from 480 to

1200 mm, by exploiting MODIS land products and meteorological

variables (i.e., air temperature (Ta), humidity (RH), pressure (P), and

radiation components (Rs, Rl, and Rn)) derived from the MODIS-MM5

FDDA over the Geum river basin (Fig. 8). The use of combined MODIS-

based and FDDA-based daily ET for cloudy days or missing pixels

showed the potential to compensate for weaknesses in optical and

thermal satellite data such as MODIS. The accordance between

MODIS-based ET and FDDA-based ET showed a high reliability

(Fig. 7), suggesting that the FDDA-based ET might show uncertainties

similar to those of MODIS-based ET in comparison with flux ET

measurements. The procedure of FDDA-based ET also followed the

same revised RS-PM algorithm with MODIS land products. Thus, any

improvement on ET estimations at either the site or the regional scale

should address the enhancement of ET algorithms, more accurate

input variables, and robust parameterization (especially for aerody-namic and canopy conductance).

5. Conclusion

In this study, we used various Aqua-MODIS products, including

atmospheric (air and dew-point temperature, total ozone burden,

surface pressure, total precipitable water vapor, and aerosol optical

depth) and land surface data (LST, emissivity, VI, and albedo) to

estimate ET. A major weakness of passive satellite data was the

relatively low retrieval rate due to cloud cover. In particular, aerosol

and albedo products were identified as bottlenecks to ultimately

determine retrieval rates of Rs and ET. Devising simple gap-filling

approaches for aerosol and albedo products improved (nearly

doubled) the retrieval rate of Rs and ET (Table 3).The input variables of ET retrieved from MODIS showed a good

agreement with ground observations for clear sky conditions. The

comparison with flux measurement ET showed meaningful errors in

MODIS-derived ET at the four flux measurement sites. The cause of

this bias was likely the difference in canopy conductance (Fig. 9). A

more enhanced parameterization of conductance data in algorithms is

required.

The approach for continuous monitoring of ET using MODIS land

products and MODIS-MM5 FDDA was successfully implemented for

the Geum river basin using satellite data for 2006. For the cropland,

annual accumulated ET (mm) showed a higher value compared to

other vegetation cover types. The high ET was influenced by the

maximum leaf conductance for the cropland. Our current algorithm,

however, is more suitable for dry crops than for rice paddy fields,

which is a dominant crop in East Asia. Most of all, consideration of

evaporation from the free water surface in rice paddy soils caused by

irrigation is be needed to improve the prediction.

Our results indicate that MODIS can be applied to monitor land

surface energy budgets and ET with reasonable accuracy and the

MODIS-MM5 FDDA has the potential to provide reasonable input data

for ET estimation under cloudy conditions. However, the issues of

spatial scale mismatch (resolution) between flux footprints, MODIS,

and MM5, as well as the temporal scale mismatch (time lag) stillremain and should be the focus of future research.

Acknowledgements

We greatly appreciated the comments and editorial correctionsfrom

Jeffrey Owen. This research was supported by grants from the

Sustainable Water Resources Research Center of the 21st Century

Frontier Research Program (Grant Code: 1-8-3), the Basic Research

Project(07-3211) of KIGAM,the InnovativeForest DisastersR&D Center

(S210809L010140) of Korea Forest Service, and the A3 Foresight

Program (CarboEastAsia) of KOSEF.

Appendix A. Calculation of ET parameters and daily ET

A.1. Canopy resistance

1

rs= Cc = fs LAI gc A1

gc = CL mTmin mVPD A2

mTmin =

1:0 Tmin Tmin openTminTmin close

Tmin openTmin closeTmin close b Tmin b Tmin close

0:1 Tmin Tmin close

8>>>>>>>:

A3

mVPD =

1:0 VPD VPDopenVPDcloseVPD

VPDcloseVPDopenVPDopen b VPD b VPDclose

0:1 VPD VPDclose

8>>>>>>>:

A4

A.2. Aerodynamic resistance

ra =rc rrrc + r

A5

rc = rtotc rcorr A6

rtotc= 107:0 A7

rcorr =1:0

273:15 + Ta293:15

1:75

101300P1

A8

rr = cp

4:0

T3

a

A9



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A.3. Calculation of daily ET (DET) using a sinusoidal model

Rnt = Rn;max sinttrise

tsettrise

A10

Rn;max =INR

sintoverpasstrise

tsettrise

h i A11

DANR =t sett riseRntdtt sett risedt

=2Rn;max

=

2INR

sintoverpasstrise

tsettrise

h i A12

DET = DANR Dlength IET

INR

0:0036

2:5A13

where 0.0036 and 2.5 are unit conversion coefficients.

Appendix B. Calculation of radiation components

B.1. Shortwave radiation (bird's clear sky model)

B.1.1. Direct irradiance

Idir = I0cosz0:9662TRTOTUMTWTAA14

TR = expf0:0903M

0:841 + M

M1:01g A15

TO = 10:1611XO1 + 139:48XO0:3035

0:002715XO1 + 0:044XO + 0 :0003X2O1

A16

XO = UOM A17

TUM = exp0:0127M

0:26 A18

TW = 12:4959XW1 + 79:034XW0:6828

+ 6 :385XW1

A19

XW = UWM A20

TA = exp0:873A 1 + A

0:7088A M

0:9108A21

A = 0:2758A;0:38 + 0:35A;0:50 A22

M = cosz+ 0:1593:885z1:251 A23

M

= MP= P0 A24

B.1.2. Diffuse irradiance

Idif = I0cosz0:79TOTWTUMTAA0:51TR + Ba1TAS1M + M1:02

A25

TAA = 1K11M + M1:06

1TA A26

TAS = TA = TAA A27

Ba = 0:51 + cosz A28

B.1.3. Total solar irradiance

Rs = Idir + Idif = 1gsA29

S = 0 :0685 + 1Ba1:0Tas A30

Tas = 100:045M0:7

A31

B.2. Longwave radiation

B.2.1. Downward longwave radiation

Rl = aT4

a

A32

a = 11 + exp 1:2 + 30:5

n o

A33

= 46:5eaTa

A34

ea = esTd = 0 :611 exp17:3 Td

Td + 237:3

A35

B.2.2. Upward longwave radiation

Rl = sT4

s

A36

s = 0:273 + 1:778311:80731321:03732 + 1:774232 A37

Appendix C. Notations in Appendices A and B

g ground albedo

s sky albedoBa ratio of the forward-scattered to the total scattered

irradiance due to aerosols (0.84)

Cc canopy conductance (mm s1)

CL maximum leaf conductance (mm s1)

cp specific heat capacity of air (J kg1 K1)

DANR daily average net radiation (MJ day1)

DET daily evapotranspiration (mm day1)

Dlength daytime length in hour (i.e. trisetset)ea actual vapor pressure (Pa)

a air emissivity

31 emissivity in MODIS band 3132 emissivity in MODIS band 32

es saturated vapor pressure (Pa)

s surface emissivity

fs shelter factor

gc stomatal conductance (mm s1)

Idif diffuse irradiance (W m2)

Idir direct irradiance (W m2)

IET instantaneous evapotranspiration (W m2)

INR instantaneous net radiation (W m2)

I0 solar constant (1353.0 W m2)

K1 constant of aerosol absorptance (0.1)

LAI leaf area index (m2 m2)

air density (kg m3)

M air mass (g cm1

)M pressure-corrected air mass

P surface pressure (mb)

P1 atmospheric pressure (Pa)

P0 normal atmosphere (1013 mb)

ra aerodynamic resistance (s m1)

rc resistance to convective heat transfer (s m1)

rcorr correction coefficient for rtotc

Rl downward longwave radiation (W m2)

Rl upward longwave radiation (W m2)

Rn net radiation (W m2)

Rn,max daily maximum net radiation (W m2)

rr resistance to radiative heat transfer (s m1)

rtot total aerodynamic resistance (s m1)

rtotc constant for rtot(107 s m1

)



16/17

RS Total solar irradiance (W m2)

SteffanBoltzmann const, 5.67108 (W m2 K4)

Ta air temperature (C)

TA aerosol transmittance

TAA transmittance of aerosol absorptance

TAS transmittance of aerosol scattering

Tas dry air transmittance

A broadband aerosol optical depth in a vertical path

A,0.38 AOD in a vertical path at 0.38 mA,0.50 AOD in a vertical path at 0.50 mTd dew-point temperature (C)

Tmin minimum temperature (C)

TO transmittance of ozone absorption

toverpass MODIS overpass time

TR transmittance of Rayleigh scattering

trise local sunrise time (h)

Ts surface temperature (C)

tset local sunset time (h)

TUM transmittance of uniformly mixed gases

TW transmittance of water vapor absorption

UO ozone amount (atm cm)

UW precipitable water in a vertical path (cm)

VPD vapor pressure deficit (Pa)

precipitable water content of atmosphereXO total amount of ozone in slanted path (cm)

XW precipitable water in a slanted path (cm)

z solar zenith angle (degree)

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