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7/27/2019 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]7/27/2019 Mapping Evapotranspiration Using Modis .. (1)
2/17
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
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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).
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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).
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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
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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.
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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.
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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).
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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
)
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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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