Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains

ent 103 (2006) 1–15www.elsevier.com/locate/rse

Remote Sensing of Environm

Estimation and comparison of evapotranspiration from MODIS and AVHRRsensors for clear sky days over the Southern Great Plains

Namrata Batra a, Shafiqul Islam b,⁎, Virginia Venturini c, Gautam Bisht d, Le Jiang e

a Department of Civil and Environmental Engineering, University of Cincinnati, P.O. Box 210071, Cincinnati, OH 45221-0071, USAb Department of Civil and Environmental Engineering, Tufts University, 113 Anderson Hall, Medford, MA 02155, USAc Facultad de Ingenieria y Ciencias Hidricas, Universidad Nacional del Litoral, C.C. 217, Santa Fe, 3000. Argentina

d Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USAe IMSG at NOAA/NESDIS, NOAA Science Center, Camp Springs, Maryland 20746, USA

Received 15 June 2005; received in revised form 9 February 2006; accepted 26 February 2006

Abstract

Evapotranspiration (ET) cannot be measured directly from satellite observations but remote sensing can provide a reasonably good estimate ofevaporative fraction (EF), defined as the ratio of ET and available radiant energy. It is feasible to estimate EF using a contextual interpretation ofradiometric surface temperature (To) and normalized vegetation index (NDVI) from multiple satellites. Recent studies have successfully estimatednet radiation (Rn) over large heterogeneous areas for clear sky days using only remote sensing observations. With distributed maps of EF and Rn, itis now possible to explore the feasibility and robustness of ET estimation from multiple satellites. Here we present the results of an extensive inter-comparison of spatially distributed ET and related variables (NDVI, To, EF and Rn) derived from MODIS and AVHRR sensors onboard EOSTerra, NOAA14 and NOAA16 satellites respectively. Our results show that although, NDVI and To differ with the sensor response functions andoverpass times, contextual space of NDVI–To diagram gives comparable estimates of EF. The utility of different sensors is demonstrated byvalidating the estimated ET results to ground flux stations over the Southern Great Plains with a root mean square error of 53, 51 and 56.24 Wm−2,and a correlation of 0.84, 0.79 and 0.77 from MODIS, NOAA16 and NOAA14 sensors respectively.© 2006 Elsevier Inc. All rights reserved.

Keywords: Evapotranspiration; Net radiation; MODIS; AVHRR

1. Introduction

Evapotranspiration (ET) is an important variable for waterand energy balances on the earth's surface. Understanding thedistribution of ET is essential for many environmentalmonitoring applications including water resources manage-ment, agricultural efficiency, global vegetation analysis, climatedynamics, and ecological applications. The three major factorscontrolling ET are availability of water, amount of availableradiant energy and transport mechanism to remove the watervapor away from the surface. The above mentioned factors inturn depend on other variables such as soil moisture, landsurface temperature, air temperature, vegetation cover, vapor

⁎ Corresponding author. Tel.: +1 617 627 4290.E-mail address: [email protected] (S. Islam).

0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2006.02.019

pressure, wind speed, etc., which again may vary with region,season, and time of day. The general approach to account for allsuch factors is to use a combination of remote sensing data,ancillary surface data and atmospheric data for the estimation ofET (Bastiaanssen et al., 1996; Jackson et al., 1977; Holwill &Stewart, 1992; Moran et al., 1994; Nishida et al., 2003; Normanet al., 2003). Accurate characterization of global ET distributionwith satellite remote sensing with little or no ground observa-tions is a challenging task. A direct estimation of evaporativefraction (EF), defined as the ratio of ET and available radiantenergy, has been shown to work well with AVHRR sensoronboard NOAA14 satellite and MODIS sensor onboard EOS-Terra satellite (Jiang & Islam, 2001, 2003; Islam et al., 2002,2003; Venturini et al., 2004).

Earlier studies focused on estimation of EF rather than ETbecause accurate estimation of available radiant energy was

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http://dx.doi.org/10.1016/j.rse.2006.02.019

2 N. Batra et al. / Remote Sensing of Environment 103 (2006) 1–15

difficult (Nishida et al., 2003; Venturini et al., 2004). Here wepropose an algorithm that uses a net radiation (Rn) estimationmethodology, recently proposed by Bisht et al. (2005), incombination with a distributed map of EF to obtain ET mapsover large areas.

Spectral response function (SRF) is defined as the sensorresponse of a channel as a function of wavelength. There areimportant differences in SRF, sensor characteristics, calibrationtechniques and atmospheric corrections (Venturini et al., 2004;Huete et al., 2002; Justice et al., 2002; Trishchenko et al., 2002)among the sensors used to estimate EF, Rn and ET. To provide acontinuous monitoring capability for ET, it is important toexplore the utility of an ET estimation algorithm with differentsensors. Here, we will test and validate the adequacy of our ETestimation algorithm over Southern Great Plains (SGP) of theUnited States for clear sky days using MODIS and AVHRRsensors onboard EOS Terra, NOAA14 and NOAA16 satellitesrespectively.

2. Estimation of ET using remote sensing

In this study, we will use an ET estimation methodologybased on an extension of the Priestley–Taylor equation and arelationship between remotely sensed surface temperature andvegetation index proposed by Jiang and Islam (2001) as:

kET ¼ /D

Dþ g

� �ðRn−GÞ ð1Þ

where λET is the representative of ET (Wm−2), Rn is the netradiation (Wm−2), G is the soil heat flux (Wm−2), Φ is theparameter that accounts for aerodynamic and canopy resis-tances, Δ is the slope of saturated vapor pressure at airtemperature (Ta) and γ is the psychrometric constant. A keyadvantage of using this methodology is that all of the fourquantities (Φ, Rn, G, and Δ) can be derived independently usingprimarily remotely sensed data. A detailed description of theseparameters may be found in Jiang and Islam (1999; 2001). Thebasis for the proposed methodology is the existence of aphysically meaningful relationship between Φ values and acombination of remotely detectable spatial parameters, To andNDVI. The scatter plot of To versus NDVI comes out to belargely bounded by a triangular domain whose boundaries areinterpreted as limiting surface fluxes (Carlson & Ripley, 1997;Carlson et al., 1995; Price, 1990; Gillies et al., 1997; Jiang &Islam, 1999). An extensive discussion on the triangular domainis given by Price (1990), Gillies and Carlson (1995), and Gillieset al. (1997). In the NDVI–To scatter plot, for the pixels withNDVI greater than 0 (i.e. no water body or cloud pixels), theland surface type ranges from bare soil to densely vegetatedsoil. Within each type of land surface, the surface temperatureranges from a minimum where there is strongest evaporativecooling to a maximum where the evaporative cooling isweakest. It is assumed that the variation in the available radiantenergy (Rn−G) is small for a particular vegetation type withinthe remotely sensed domain. It is important to note that in theabsence of significant advection and convection, latent heat flux

(ET) cannot exceed the available radiant energy (Rn−G) andhence Φ has a limited range between 0 and (Δ+γ) /Δ. Avalue of0 for Φ corresponds to pixels with no evaporation while a valueof (Δ+γ) /Δ for Φ corresponds to pixels with maximumevaporation. Maximum value of Φ is assigned to 1.26 usingPriestley–Taylor's α parameter for evaporation under equilib-rium wet surface conditions (Eichinger et al., 1996). Anadvantage of using Φ is that aerodynamic and canopyresistances are not explicitly needed for large heterogeneoussurfaces since Φ takes a wider range of values (0–1.26) than thatof Priestley–Taylor's parameter α (1.26) which is suitable onlyfor wet surface conditions.

Fig. 1 illustrates an example of the interpolation scheme usedto obtain Φ value for each pixel in a remotely sensed image. Theglobal minima and maxima of Φ are determined as Φmin=0 forthe driest bare soil pixel and Φmax=1.26 for the denselyvegetated pixel. Given the two bounds (NDVI=0, Φmin),(NDVI=NDVImax, Φmax) and the range of NDVI, the Φmin

i canbe linearly interpolated for each NDVI interval (NDVIi). TheΦmaxi for each NDVIi can be obtained from the pixel

corresponding to the lowest surface temperature within eachNDVI interval. After the lower and upper bounds of Φ valuesfor each NDVI class have been determined, the next step is tointerpolate within each NDVI class between the lowesttemperature pixels, i.e. the pixels with highest evaporationwithin this NDVI class (Tmin

i , Φmaxi ), and the highest

temperature pixel where the evaporation is lowest within thisNDVI class (Tmax

i , Φmini ). Consequently, the Φ values for each of

the pixel can be determined using the spatial context of remotelysensed surface parameters To and NDVI. In simpler terms, Tmin

is the lowest temperature at full vegetation cover and can bedetermined by the mean in-land water surface temperature andTmax is obtained by extrapolating the warm edge to intersectwith NDVI=0, which is virtually the highest temperature overthe bare soil. Linear interpolation of Φ with temperature leads tonormalization of surface temperature and is given as:

U ¼ UmaxTmax−ToTmax−Tmin

ð2Þ

Evaporative fraction (EF), defined as the ratio of ET andavailable radiant energy is evaluated as:

EF ¼ UD

ðDþ gÞ ð3Þ

where Δ is the slope of saturated vapor pressure at Ta, defined byMurray (1967) as:

D ¼ 26297:76ðTa−29:65Þ2 exp

17:67ðTa−273:15ÞTa−29:65

� �ð4Þ

Since Ta is obtained from limited surface meteorologicalobservation stations, earlier studies used interpolated Ta valuesfor the remaining pixels in the image. Also, the effect oftemperature being implicit in the estimation of Δ, the quasi-linear relationship between To and To–Ta can be used to infer Ta(Bastiaanssen, 1995; Jiang & Islam, 2001). Nishida et al. (2003)used Ta as the surface temperature of a full vegetation canopy;

Fig. 1. Linear interpolation scheme to obtain ‘Φ’ from NDVI–To scatter plot of a clear sky day.

3N. Batra et al. / Remote Sensing of Environment 103 (2006) 1–15

however this is not true under extremely active or depressed ET.In this study, we obtained spatially distributed Ta derived fromMODIS Atmospheric profile product (MOD07) which uses astatistical regression retrieval algorithm to determine thetemperature profiles, using previously determined statisticalrelationship between the observed radiances and correspondingatmospheric profiles (Menzel et al., 2002). The comparison of Φor EF map provides a similar insight since the same source of Tais used for calculation of Δ in deriving EF values from multiplesensors.

The EF has been found, from several observational studies,to be fairly time invariant during the daytime (e.g., Shuttleworthet al., 1989; Brutsaert & Chen, 1996; Crago & Brutsaert, 1996).Crago and Brutsaert (1996) concluded that a combination ofweather conditions, soil moisture, topography, and biophysicalconditions contribute to the conservation of EF. Brutsaert andSugita (1992) showed that cumulative daytime evaporationlosses could be accurately estimated by multiplying aninstantaneous estimate of EF by the cumulative daytime Rn.

2.1. Estimation of net radiation using MODIS data

There have been many attempts to estimate net radiation (Rn)by combining remotely sensed observations with ancillarysurface and atmospheric data (Gautier et al., 1980; Diak &Gautier, 1983; Jacobs et al., 2000; Ma et al., 2002). Islam et al.(2003) obtained net radiation by interpolation of groundobservations. Recently developed methodology of Bisht et al.(2005) eliminates the need for ancillary ground data to provide aspatially distributed instantaneous and diurnal cycle of netradiation map over a large heterogeneous domain for clear skydays. We present the algorithm to estimate Rn briefly here and

for a detailed description of the algorithm readers are directed toBisht et al. (2005). Instantaneous Rn (Wm−2) can be evaluatedin terms of its components of downward and upward short waveradiation fluxes and downward and upward long wave radiationfluxes as:

Rn ¼ RAs−R

zs þ RA

L−RzL ¼ ð1−aÞRA

s þ RAL−R

zL ð5Þ

where Rs↓ and Rs

↑ are short wave radiation fluxes downward(Wm−2) and upward (Wm−2) respectively and RL

↓ and RL↑ are

the long wave radiation fluxes downward (Wm−2) and upward(Wm−2) respectively; α is the surface albedo. The methodologyutilizes Zillman's (1972) parameterization scheme to obtaindownward short wave radiation; Prata's (1996) parameteriza-tion scheme along with air temperature to obtain downwardlong wave radiation; land surface emissivity and surfacetemperature to estimate upward long wave flux respectively.Instantaneous net radiation (INR) estimates are obtained usingvarious MODIS land products (land surface temperature, landsurface emissivities and land surface albedo) and MODISatmospheric data products (air temperature, dew temperatureand aerosol depth).

Bisht et al. (2005) proposed a sinusoidal model to estimatethe diurnal cycle of net radiation, which closely follows theframework of Lagouarde and Brunet's (1993) model of thediurnal cycle of surface temperature. The sinusoidal model isgiven as:

RnðtÞ ¼ Rn maxsint−trisetset−trise

� �k

� �ð6Þ

where Rn_max is the maximum value of Rn estimated during theday, trise and tset are the local times at which Rn becomes positive

Table 1Bandwidth of SRFs of AVHRR and MODIS sensors (Trishchenko et al., 2002)

Channelname

AVHRRband #

NOAA16–AVHRRbandwidth(μm)

NOAA14–AVHRRbandwidth(μm)

MODISband #

MODISbandwidth(μm)

Red 1 0.58–0.68 0.58–0.72 1 0.62–0.67Near IR 2 0.72–1.00 0.68–1.10 2 0.84–0.87ThermalIR

4 10.32–11.32 10.32–11.32 31 10.78–11.28


and negative respectively. For a given study day, satelliteoverpass time (toverpass), sunrise time (trise) and sunset time (tset)are known, the value of Rn_max can be obtained fromcorresponding value of INR estimate. The diurnal cycle of netradiation or Rn at any time ‘t’ can then be estimated using Eq.(6). In this study, INR or Rn (t) is obtained from MODIS usingBisht et al. (2005) methodology at Terra overpass time forselected clear sky days. The fifth and sixth columns of Table 2contain the sunrise and sunset time for the study region,obtained from the website of US Naval Observatory, Astro-nomical Application Department (http://aa.usno.navy.mil/). Ithas been observed that Rn generally starts to increase about45 min after sunrise (trise) and it becomes less than zero about45 min before sunset (tset) for the entire range of study period. Itmay be noted that the value of sunrise and sunset time for anygiven day was chosen for the whole domain (shown in Table 2)as their variation over the domain was less than 15 min.Though, Rn is composed of short wave radiation flux (upwardand downward) and long wave radiation flux (upward anddownward), all of which vary in different ways, we are hereonly concerned with the net radiation which varies in asinusoidal manner.

2.2. Estimation of soil heat flux

Soil heat flux (G) is variable with time of day but itsmagnitude is very small as compared to Rn or λETand hence wecan assume G as constant over the day with negligible errors. Aremote measurement of G may not be feasible but a relationbetween G/Rn and spectral data in the red and near infrared(NIR) wavebands can be determined using the scheme of Moranet al. (1989) for the time of overpass as:

G ¼ 0:583expð−2:13NDVIÞRn: ð7Þ

3. An overview of sensors

For continual, comprehensive global coverage, a series ofadvanced satellite sensors are being designed, developed andlaunched over the last several decades. Among various satellitesensors, AVHRR, onboard NOAA polar orbiting satellites hasone of the longest records of operation. The AVHRR sensor is abroadband, 5- or 6-channel scanning radiometer, sensing in thevisible, near-infrared (NIR), and thermal-infrared (TIR) por-tions of the electromagnetic spectrum. The AVHRR sensorprovides for global (pole to pole) on board collection of datafrom all spectral channels. Each pass of the satellite provides a2399 km wide swath, giving a spatial resolution of 1.1 km. Thesatellite orbits the Earth 14.1 times each day from 833 km aboveits surface. All the channels of AVHRR have been used tocharacterize different land surface attributes. For example,AVHRR channels 1 and 2 are used to estimate NDVI andchannel 4 is used to estimate To (Venturini et al., 2004; Gleasonet al., 2002; Trishchenko et al., 2002; Myneni et al., 1995).Channel 4 brightness temperature being very low is able toindicate that the satellite is seeing cloud-top temperatures. Thus

if the temperature of a pixel is less than 273 K, the pixel isconsidered contaminated. Bandwidth of channel 4 in AVHRR isvery close to the bandwidth of channel 31 of MODIS. Althoughthe construction and characteristics of all AVHRR instrumentsare quite similar, they are not identical among all NOAAmissions. The newly launched NOAA16 satellite includesimprovements to instruments that are revolutionary andsignificant. Cloud masking is done in an automatic way by aprocedure developed by Saunders and Kriebel (1988), resultingin an accurate number of clear pixels. Instrument payload isincreased with improvements in imaging and soundingcapabilities. A 10 bit radiometric sensitivity can be achievedas compared to 8 bit sensitivity of AVHRR on NOAA14. Scanmechanism lifetime and jitter performance of AVHRR havebeen improved with changes to lubricants, motors and bearings.A large external sun shield has been added to the scan motorhousing to reduce sunlight impingement and associatedcalibration problems that may have led to the degradation ofAVHRR onboard NOAA14 in late 2001 (NOAA KLM guide:www2.ncdc.noaa.gov/docs/klm/). We also explore the utility ofMODIS, onboard EOS-Terra satellite which was launched inDecember 1999, at an altitude of 705 km, with a globalcoverage every one or two days. The MODIS instrumentprovides high radiometric sensitivity (12 bit) in 36 spectralbands between 0.4 to 14.38 μm whose spatial resolutions rangeis from 250 to 500 and 1000 m. Spectral response function(SRF) of MODIS and AVHRR sensors for channels in visible,near infrared (NIR) and thermal infrared (TIR) wavelengthsdiffer in shape, band-width, central wavelength location and thedegree of overlap between channels (Trishchenko et al., 2002).The bandwidth of spectral response functions of red, NIR andTIR channels of the three sensors is listed in Table 1. It mayparticularly be noted that the channels of the new AVHRRonboard NOAA16 have narrower bandwidths and a muchsmaller overlap over the vegetation transition band, which leadsto higher NDVI values as compared to those recorded byAVHRR on NOAA14. The MODIS channels on the other handare much narrower and have no overlap with each other. We willexplore how these differences affect the estimation of ET.

Data from AVHRR sensors onboard NOAA14 and NOAA16satellites are obtained from NOAA Satellite Active Archive(SAA) comprehensive large array-data stewardship system(http://www.class.noaa.gov/nsaa/products/welcome) in High-Resolution Picture Transmission format. To access NOAAfiles which are generated over IBM machines, there is a changein byte order and data acquisition becomes difficult over other

http://aa.usno.navy.mil/

http://www2.ncdc.noaa.gov/docs/klm/

http://www.class.noaa.gov/nsaa/products/welcome

Fig. 2. Distribution of Bowen ratio stations (denoted with E) over SGP on aUSGS land cover map.


heterogeneous platforms. Data is extracted as per the guidelinesof NOAA Polar Orbiter Data (POD) and NOAA KLM user'sguide which describes the orbital and spacecraft characteristics,instruments, data formats, etc. of NOAA14 and NOAA16satellites respectively. MODIS data products are obtained fromEOS Data Gateway in a Hierarchical Data Format (HDF) whichis the standard data storage format for MODIS. HDF has theadvantage of sharing self-describing files across heterogeneousplatforms. The data used for the study (see http://edcimswww.cr.usgs.gov/pub/imswelcome) consist of MODIS level-2 data:MOD02 (calibrated radiances), MOD03 (latitude, longitude andsolar zenith angle), MOD04 (aerosol depth), MOD07 (air anddew temperature) and MOD11 (land surface temperature andemissivity), and MODIS level-3 data: MOD43B3 (land surfacealbedo). MOD02 contains calibrated and geolocated at-apertureradiances for 36 bands that can be processed further to evaluateNDVI and To. The MODIS geolocation dataset, (MOD03)contains geodetic coordinates, ground elevation, solar andsatellite zenith, and azimuth angle for each MODIS 1-km pixel.The MODIS atmospheric profile product (MOD07) is availableat 5×5 km resolution, at 20 vertical atmospheric pressure levels.In the present study, air and dew point temperature at verticalpressure level of 1000 hPa, are taken as surrogate for thetemperatures at screen level height. Also the temperatures areassumed to be homogeneous over the 5×5 km grid. MODISaerosol product (MOD04) is available at a spatial resolution of10×10 km pixel array. In the current study, the aerosol depthwas assumed to be homogeneous over the 10×10 km grid andwas estimated for the 0.55 μm wavelength. MOD11 containsland surface temperature (LST) and band emissivities (for bands31 and 32) at 1 km resolution for clear sky days. The MODISbidirectional reflectance distribution function (BRDF) andalbedo product, called MOD43B is a standard MODIS dataproduct produced every 16 days at 1 km spatial resolution andarchived as equal area tiles representing of 1200×1200 pixels insinusoidal (V004) projection. The land surface albedo isassumed constant during this 16-day interval. In the currentstudy, MOD43B3 V004 data product is used, which consist ofblack- and white sky albedos for seven spectral bands (band 1–7) and the three broadbands (0.3–0.7, 0.7–3.0 and 0.3–5.0 μm).

4. Study site and selection of days

4.1. Study region

The Southern Great Plains (SGP) region of US was chosen asour study site because of its relatively flat terrain, heterogeneousland cover, easy accessibility, wide variability of climate cloudcover type and surface flux properties, and large seasonalvariation in temperature and specific humidity. It covers most ofthe Oklahoma and southern part of Kansas, extending inlongitude from 95.3°W to 99.5°W and in latitude from 34.5°Nto 38.5°N. Fig. 2 shows the study region. It is a heterogeneousland cover area characterized by mixed farming, interruptedforest, tall and short grass. Major soil types are silty loam,loamy sand and loam. The region produces much of the nation'sgrain and fiber, including over 60% of the wheat and 36% of the

cotton. Water supplied as irrigation in the region amounts tobillions of cubic meters (2×1010 m3 in 1990) annually and isgoverned by the determination of ET in the region.

The pre-defined projection grid of SGP domain is dividedinto 467 columns by 444 rows. Retrieved data is resampled tothe following grid with an approximate pixel resolution of1.0 km. This region has the relatively extensive and welldistributed coverage of surface flux and meteorologicalobservation stations. In this study, Energy Balance BowenRatio (EBBR) stations, maintained by the AtmosphericRadiation Measurement (ARM) program are used for thevalidation of surface fluxes. The stations are widelydistributed over the whole domain as shown in Fig. 2 overthe land cover map of SGP derived from using 1-km globalUSGS data (http://edcsns17.cr.usgs.gov/glcc/); stations E9,E13, E15 are located in crops and mixed farming region; E19,E24 in short grass region; E7 and E12 in tall grass region; E4,E8 and E11 in ungrazed rangeland and E20 is in interruptedforest region.

4.2. Selection of clear sky day images

To meet our objective of doing a comparative study; data arecollected from AVHRR and MODIS sensors onboard

http://edcimswww.cr.usgs.gov/pub/imswelcome

http://edcimswww.cr.usgs.gov/pub/imswelcome

http://edcsns17.cr.usgs.gov/glcc/

Table 3Percentage of clear pixels on selected days from multiple sensors

%Clearpixels

MODIS NOAA16 NOAA14 MODISandNOAA16

NOAA16andNOAA14

MODISandNOAA14

DOY115 94.82 96.96 89.01 84.13 89.53 86.38163 83.65 89.53 87.95 82.19 83.14 80.30190 84.92 78.83 83.56 81.24 80.56 80.05


NOAA14, NOAA16 and EOS-Terra satellites respectively. Theproposed ET estimation approach is applicable mainly for clearsky conditions. Acquisition of clear sky day images with all thesensors simultaneously is not easy. Various factors are to betaken into account for choosing a clear sky day image. As apreliminary step, the days are chosen on the basis of dataavailability and visual inspection of a cloud-free image from theNOAA and NASA's EOS websites respectively. Images arefirst calibrated, converting radiances in the visible bands(channels 1 and 2) to spectral albedo values and radiances inthermal band (channel 4 for AVHRR and channel 31 forMODIS) to blackbody temperature. Images are then processedto determine cloud-contaminated pixels to exclude them fromfurther analysis though we get images after the application ofinbuilt cloud mask functionalities of MODIS and AVHRRsensors. A cloud masking threshold (To<273 K or NDVI<0) isthen applied to remove cloudy pixels. As it is not possible toavoid cloudy pixels over the whole domain, we characterize80% of the study area being clear as a clear sky condition for ourmethodology. Table 2 gives the selected days and overpasstimes of multiple satellites over SGP for the study of inter-comparison of sensors. Table 3 gives an overview of thepercentage of clear pixels for a given day with different sensorsfor inter-comparison. Our comparative study with three sensors,comprising of sensors onboard EOS Terra, NOAA14 andNOAA16 satellites, is possible only for the year 2001 when allthe three satellites were operational. After an extensiveevaluation of a number of days, three days were identified(DOY: 115, 163, 190) in 2001, starting from late spring tosummer that satisfy the clear sky condition at the overpass timeof all three satellites. Further comparisons with more number ofdays are needed before generalized conclusions can be made ofthe proposed methodology and reliability of sensors. Threemore days were analyzed to satisfy the clear sky condition forMODIS and NOAA14 overpass time, giving us a total of sixdays (DOY: 115, 163, 190, 229, 276, 287) in 2001 for analysisof ETwith these two sensors. After the degradation of NOAA14in November 2001, much attention was given to explore the

Table 2Satellite overpass, sunrise and sunset time (standard time) for the study days

Calendar day(day of year)

MODISoverpasstime

NOAA16overpasstime

NOAA14overpasstime

Sunrisetime

Sunsettime

25th April '01 (115) 11:20 13:53 16:00 5:45 19:1612th June '01 (163) 11:20 13:55 16:28 5:12 19:529th July '01 (190) 11:00 14:15 16:54 5:20 19:5417th Aug '01 (229) 11:05 16:41 5:51 19:213rd Oct '01 (276) 11:00 17:20 6:29 18:1214th Oct '01 (287) 11:40 16:46 6:38 17:581st April '02 (091) 11:30 13:57 6:18 18:549th April '02 (099) 10:40 14:12 6:06 19:0114th Oct '02 (287) 11:05 13:14 6:39 17:5723rd March '03 (082) 11:05 13:41 6:29 18:4331st March '03 (090) 11:55 13:52 6:17 18:151st April '03 (091) 11:00 13:41 6:15 18:5119th Sept '03 (262) 11:40 13:28 6:14 18:3112th Oct '03 (285) 10:45 14:12 6:33 17:5719th Oct '03 (295) 10:50 14:34 6:40 17:48

utility of NOAA16 in order to maintain the continuity of datafor climate forecasting and research applications. To compareMODIS and NOAA16 sensors, we have identified three days(DOY: 91, 99, 287) in 2002 and six days (DOY: 82, 90, 91, 262,285, 292) in 2003. Consequently, we have a total of fifteen daysranging from early spring to late fall for three different years tocompare and contrast the estimation of ET.

There are no generally accepted methodologies to compareand validate the spatial distribution of ET over large areasbecause of the scale mismatch between the estimated ET mapand point observations from the ground. Typically, avalidation exercise involves comparing model output to fluxstation observations at a handful of sites, and hence it isdifficult to evaluate the reliability of model output for theremaining pixels in an image. In addition, there are issues ofmixed pixel and registration error between the remotelysensed pixel and surface observations. Random measurementerror arises at some points in these stations on account ofvaried usage of sensors, measurement techniques and systemcalibration methods (Jiang & Islam, 2001). The measurementerrors should also be kept in mind while doing suchcomparisons. Discontinuity or randomness of ground stationdata may be attributed to cloud cover, instrument fault orother obstructions.

5. Results

5.1. Inter-comparison of To, NDVI, Φ and ET by MODIS,NOAA14 and NOAA16 sensors

We compare and contrast the spatially varied parametersused in the determination of ET over SGP, keeping in mind thedistinct characteristics of multiple sensors and the respectivespectral response functions of their channels. The adoptedmethodology is based on a contextual interpretation of thetriangle space of To and NDVI. Hence, we begin with thedetailed analysis of remotely sensed parameters: To and NDVI,followed by the coefficient ‘Φ’ which gives us an estimate ofEF. A spatially distributed net radiation map derived fromMODIS is then used to evaluate ET from each sensor. EstimatedET values are then validated to ground observations. Table 2displays the three selected days (DOY: 115, 163, 190) in 2001with corresponding overpass time of three sensors for inter-comparison. Table 3 shows the percentage of clear pixels witheach sensor separately on a given day. In all the cases ofcomparison statistics, only common clear pixels were useddisregarding the fill values.


To and NDVI derived from MODIS and AVHRR sensorsfor the three selected days in 2001 are compared in terms ofmaximum, minimum, mean and standard deviation in Table 4.In general, To is found to be the highest with NOAA16,followed by MODIS and NOAA14. The differences of about25 K in To are consistent with the time gap of 2 to 3 h betweenoverpass time of satellites. Standard deviation is not more than3.8 K in any case. The mean recorded temperature iscomparable among all the sensors, despite major differencesin the sensor characteristics, overpass time, atmosphericcorrections and spectral response function of the thermalinfrared channels. Errors in extracting To from satellite dataarise from the atmospheric effects and in determining theemissivity of the surface. The retrieved NDVI values fromAVHRR on NOAA14 are consistently lower than the retrievedvalues from other two sensors, a difference of about 0.3 inmaximum recorded NDVI. While, maximum recorded NDVIfrom MODIS and NOAA16 sensors are much the same,though mean NDVI from MODIS is slightly higher than themean NDVI from AVHRR on NOAA16. It is the overlappingof red and NIR channels (in addition to the sensor degradation)of AVHRR onboard NOAA14 at chlorophyll absorption band(0.68–0.72 μm wavelength) which results in lowering of theNDVI. Bandwidths become narrower with AVHRR onboardNOAA16 with a much smaller overlap, while MODIS redband is clearly separated from the NIR band (see Table 1). Theresults are as expected in literature (Venturini et al., 2004;Huete et al., 2002; Trishchenko et al., 2002). As evidencedfrom previous studies (Price, 1990; Gillies et al., 1997; Jiang &Islam, 2001; Venturini et al., 2004), NDVI–To scatter plotsexhibit a characteristic triangular pattern for all the study days

Table 4Variation of To, NDVI, Φ and λET over SGP from multiple sensors

Date [DOY] 25th April '01 [115] 12th June '01

MODIS NOAA16 NOAA14 MODIS

To (K)Max 312.34 316.00 303.23 311.19Min 291.40 294.01 292.50 297.5Mean 301.77 304.89 297.08 303.71Std. 3.80 3.05 1.25 1.65

NDVIMax 0.80 0.83 0.58 0.80Min 0.0005 0.00 0.0001 0.0001Mean 0.48 0.42 0.32 0.50Std. 0.10 0.11 0.08 0.11

ΦMax 1.26 1.26 1.26 1.26Min 0.52 0.47 0.52 0.66Mean 0.89 0.88 0.98 1.00Std. 0.09 0.19 0.09 0.07

λET (Wm−2)Max 403.84 394.06 393.42 667.60Min 112.19 100.52 100.51 215.72Mean 242.91 231.21 241.36 440.41Std. 38.70 42.40 35.72 52.41

with different sensors. As an example, NDVI–To scatter plotsobtained from MODIS, NOAA14 and NOAA16 sensors overSGP for 25th April, (DOY: 115) 2001, are shown in Fig. 3.However, the pixel distributions within the triangular spacechange from one sensor to the other for any given day. Forexample, NOAA14 shows a displacement of more than 10 Kfor the temperature range on Y-axis and about 0.2 units overthe NDVI range on X-axis. It can be deduced that as theoverlapping between red and NIR channels increases, thetriangle tends to shift towards left. Despite the differences inrecorded To and NDVI, the method developed by Jiang andIslam (2001) is able to capture the Φ parameter, as well as EF,for each pixel with reasonable correspondence betweensatellites. Table 4 shows the variation of Φ obtained fromNDVI–To scatter plots of MODIS and AVHRR sensors.

Net radiation (Rn) obtained from MODIS is used to evaluateavailable radiant energy (Rn−G) for all the sensor systems inthis study. Regardless of the time of measurement for Φ, wecan compare EF estimates from different sensors since Φappears to be time invariant over a day. EF being fairlyconstant over a day (Hall et al., 1992; Bastiaanssen et al.,1996), the product of EF with available radiant energy enablesus to generate instantaneous ET map at any time of the day.Spatially distributed ET maps with MODIS, NOAA14 andNOAA16 sensors over SGP for a clear sky day (DOY 190) arepresented in Fig. 4. ET scales from 102 to 636 Wm−2, withhigher values clearly distinguishable on the south west of thestudy domain with all the sensors, however the extent of highET areas change from map to map, being larger for MODIS.Table 4 shows an extensive comparison statistics of ET(Wm−2). Maximum and mean recorded value of ET is higher

[163] 9th July '01 [190]

NOAA16 NOAA14 MODIS NOAA16 NOAA14

314.63 307.36 316.98 325.52 309.61300.20 299.10 298.20 302.10 300.10306.26 301.77 304.91 310.09 302.132.33 2.13 3.08 5.46 2.53

0.79 0.58 0.79 0.76 0.520.00 0.00002 0.0004 0.00 0.000010.39 0.32 0.48 0.35 0.260.11 0.11 0.11 0.11 0.09

1.26 1.26 1.26 1.26 1.260.29 0.62 0.38 0.06 0.160.84 1.07 0.97 0.82 0.910.14 0.08 0.14 0.26 0.21

636.93 628.96 698.89 658.40 588.4286.48 108.07 138.86 164.84 150.29350.43 400.82 427.86 406.62 380.6777.77 71.68 84.44 91.45 73.27

Fig. 3. NDVI–To Scatter Plots for a clear-sky day over SGP from multiple sensors.


with MODIS as also depicted in Fig. 4. Sensors are thencompared in terms of bias, root mean square error (RMSE) andcorrelation coefficient (R2) to find their association andreliability. It should be noted that for comparison betweenthe two sensors, we choose the pixels that are not under cloudcover with both the sensors. Table 3 gives the number of clearpixels from each satellite and combined clear pixels of twosatellites since cloud cover pixels may not be the same with

Fig. 4. λ ET maps for a clear-sky day

two sensors as they overpass at different times of the day.Table 5 compares and contrasts the results of To, NDVI, Φ andET. As can be expected, AVHRR sensors compare best amongthemselves for To with comparatively low values in RMSE andbias (about 0.04% of mean To), though correlation is moderate.For NDVI, MODIS and NOAA16 show best agreement interms of bias, RMSE and R2 as was expected from theirrespective spectral curves (Trishchenko et al., 2002). Error in

over SGP from multiple sensors.

Table 5Inter-comparison of To, NDVI, Φ and λET over SGP from multiple sensors

DOY MODIS, NOAA16 NOAA16, NOAA14 NOAA14, MODIS

To(K)

RMSE BIAS(MO–N16)

R2 RMSE BIAS(N16–N14)

R2 RMSE BIAS(N14–MO)

R2

115 3.68 3.03 0.74 0.12 0.09 0.68 5.34 4.90 0.63163 3.25 2.69 0.62 0.09 0.07 0.55 3.28 2.34 0.49190 6.36 5.18 0.76 0.11 0.09 0.69 3.66 2.77 0.65

NDVI115 0.12 0.07 0.60 0.12 0.09 0.60 0.18 0.16 0.61163 0.14 0.11 0.70 0.19 0.17 0.82 0.18 0.20 0.71190 0.09 0.13 0.85 0.11 0.09 0.81 0.07 0.02 0.97

Φ115 0.06 0.08 0.74 0.12 0.10 0.68 0.11 0.10 0.63163 0.17 0.16 0.69 0.23 0.21 0.74 0.20 0.07 0.70190 0.19 0.13 0.76 0.19 0.14 0.72 0.14 0.01 0.66

λET (Wm−2)115 30.15 11.38 0.77 27.31 6.80 0.75 30.70 13.89 0.73163 66.50 53.65 0.86 60.70 40.98 0.78 96.74 52.19 0.76190 43.47 8.34 0.85 53.88 19.68 0.76 85.74 16.73 0.85


ET may be approximated as a sum of errors in Φ, Δ, Rn and G.Since, we have used the same data source for the estimation ofRn and Δ (Terra, MODIS) for all the sensors, Φ is the mostdominant source of error for the comparison of our ETestimates among different sensors. Table 5 shows the RMSEand bias in Φ to be less than 0.23 for any study day with themean value being less than 1.07. It can be said, in general thatthe errors in Φ are within 23% of mean value, with correlationin the range of 0.63–0.76. Results of ET show a slightimprovement, particularly in R2 and bias as compared to Φ/EFcomparisons. It may be because most of ambiguous EF valueshappen to be under low available radiant energy. Similartendency was reported by Nishida et al. (2003). Though it isdifficult to generalize from these limited analyses, these resultsindicate general applicability of the ET estimation methodol-ogy for different sensor systems with reasonable accuracy.

5.2. Inter-comparison of To, NDVI, Φ and ET by MODIS andNOAA14 sensors

To shed more light on the comparative study of MODIS andNOAA14, second set of comparison was done with six-dayanalysis, presented in Table 6. Comparison statistics of To,

Table 6Variation of mean To, NDVI and Φ over SGP from MODIS and NOAA14sensors

DOY(2001)

MODISTo (K)

NOAA14To (K)

MODISNDVI

NOAA14NDVI

MODISΦ

NOAA14Φ

115 301.77 297.08 0.48 0.32 0.89 0.98163 303.71 301.77 0.50 0.32 1.00 1.07190 304.91 302.13 0.48 0.26 0.97 0.91229 305.01 301.89 0.45 0.33 1.09 0.74276 298.87 294.02 0.47 0.31 0.94 1.02287 295.01 287.84 0.46 0.32 1.04 0.93

NDVI and Φ are similar to the ones reported by Venturini et al.(2004) over South Florida. Mean recorded temperature isconsistently higher with MODIS. It should be kept in mind thatEOS Terra overpasses the study region during daytime at about11 AM while NOAA14 does it after 4 PM in the evening.Standard deviation is larger with MODIS, as was reported byVenturini et al. (2004). A sudden drop in temperature wasobserved during late fall due to the change of season. MODIS–NDVI values are up to 20% larger than those from NOAA14,the discrepancy in retrieved NDVI from two sensors can beexplained by the overlapping of visible and NIR bands, asdiscussed earlier and in detail by Trishchenko et al. (2002).Comparing the statistics of Φ, there is a difference of ±0.12 inmean-Φ and standard deviation is much the same for both thesensors. Φ displays a moderate to good correlation betweenthese sensors, with R2 ranging from 0.63 to 0.92 and RMSEsvary from 0.09 to 0.22. It is worthwhile to note that RMSEs andbiases in Φ are comparable to those of Islam et al. (2003) andVenturini et al. (2004) (Table 6).

ET values estimated from MODIS and AVHRR show adifference as large as 40 Wm−2 in mean ET, standarddeviation lies in the range of 30–80 Wm−2. The RMSE of ETdisplayed in Table 7 varies from 30.68–97.06 Wm−2, bias

Table 7Inter-comparison of λET from MODIS and NOAA14 sensors

DOY(2001)

MODIS λET(Wm−2)

NOAA14 λET(Wm−2)

RMSE(Wm−2)

BIAS(Wm−2)

R2

mean mean

115 230.47 253.78 30.68 13.9 0.73163 419.60 420.62 97.06 54.14 0.76190 417.16 391.36 51.33 16.74 0.85229 276.34 236.30 46.78 21.29 0.94276 245.13 265.98 42.50 22.93 0.69287 289.85 259.19 74.89 59.02 0.78

Table 8Inter-comparison of λET from MODIS and NOAA16 sensors

Day ofyear

MODIS λET(Wm−2)

NOAA16 λET(Wm−2)

RMSE(Wm−2)

BIAS(Wm−2)

R2

2001115 253.78 236.92 31.20 11.82 0.77163 435.48 341.68 65.35 52.73 0.86190 406.81 391.41 41.58 7.98 0.85

200291 225.25 214.64 33.91 10.67 0.7999 206.31 193.47 45.84 12.83 0.68287 247.17 211.12 56.89 52.97 0.82

200382 199.32 192.98 32.36 23.24 0.6390 226.71 192.99 46.60 −25.03 0.6291 205.31 199.15 48.73 42.06 0.81262 345.53 333.13 30.54 12.41 0.92285 264.58 285.20 46.29 28.03 0.87292 235.72 223.82 25.75 −12.20 0.86

Fig. 5. Ground station plot of λ ETand implementation of Sine Curve shown as atypical clear sky day case.


ranges from 13.9–59.02 Wm−2 (ET from MODIS being morethan that from NOAA14) and R2 varies from 0.69 to 0.94.Irrespective of EF, comparatively higher ET with MODIS canbe explained by the soil heat flux (G) which varies as afraction of Rn and an inverse exponential function of NDVI,given by the scheme of Moran et al. (1989). For example,NDVI of 0.5 from MODIS gives available radiant energy(Rn−G) as 0.8 Rn, while at the same point NDVI of 0.3 fromNOAA14 will give available radiant energy as 0.7 Rn, adifference of about 15%. Hence, we can deduce that higherNDVI value of MODIS tends to increase the available radiantenergy and ET.

5.3. Inter-comparison of To, NDVI, Φ and ET by MODIS andNOAA16 sensors

Using AVHRR sensors onboard NOAA16, we are able toretrieve twelve more clear sky days (Table 2). It is importantto note that the comparison results of AVHRR onboardNOAA16 are likely to be different from AVHRR onboardNOAA14 because of the significant changes in the AVHRRsensors for these two satellites. Results reveal that mean Tofrom NOAA16 is higher than MODIS (a difference of about2%) while mean NDVI of MODIS is about 25% higher thanNOAA16. Standard deviation of To ranges from 1.95–5.96 K,which seems to be acceptable. The differences may beattributed to the differences in sensor characteristics, calibra-tion techniques and corrections applied for nonlinearity.Perhaps, To corresponds better as compared to NDVI sincethermal bands are similar for the two sensors but not the redand NIR channels. MODIS NDVI is about 25% higher sincered and NIR channels are narrower than the channels ofAVHRR–NOAA16 and have no overlap with each other overthe vegetation transition band as examined by Trishchenko etal. (2002). It is to be noted that NOAA16 bandwidths are inbetween NOAA14 and MODIS bandwidths (Table 1) whichresult in a better agreement of NDVI and To from MODIS and

NOAA16 sensors as compared to MODIS and NOAA14sensors. There are differences of about 10% in meanestimated Φ between the two sensors. RMSE covers a widerange of 2–25% of mean Φ and R2 from 0.45 to 0.89.Standard deviation is not more than 0.14 in any case.Comparing the statistics of ET, values are on the high side(about 20%) for MODIS with very few exceptions. Resultsare as expected since MODIS–NDVI is also about 25%higher than NOAA16–NDVI. Having obtained spatiallydistributed ET maps from the two sensors, their relativecorrespondence is evaluated in Table 8. In terms of highcorrelation (0.62–0.92) and moderate standard error (within26% of mean ET), there appears to be good agreementbetween ET estimates of two sensors.

6. Diurnal cycle of ET

Remote sensing measurements are essentially instantaneousand so are the estimated rates of ET. It is therefore necessary todevelop techniques which can convert instantaneous values intodaily estimates of ET. A common approach is to use a weightingtechnique based on the similarity between the diurnal course ofET and that of other components of the surface energy balance(Brutsaert & Sugita, 1992). Jackson et al. (1983) assumed thatthe diurnal course of ET would generally follow the course ofsolar radiation throughout the daylight period. With theimplementation of sinusoidal model of Rn by Bisht et al.


(2005); the diurnal course of ET can be approximated by a sinefunction as follows:

ETðtÞ ¼ ETmaxsint−trisetset−trise

� �k

� �ð8Þ

The proposed sinusoidal model is similar to Lagouarde andBrunet's (1993) model, which requires information of maxi-mum ET, the time when ET starts to follow sine curve (trise) andalso the time when it tends to come back (tset) to determine ET atany time (t). The sinusoidal model of ET is first applied to theEBBR ground station observations over SGP to examine theapplicability of the proposed method. In each case, ETmax wastaken as the maximum observed ET by that particular station ona given day. In general, the ground station plots show that ETvalues tend to behave in a sinusoidal manner 2 h after thesunrise and follow the curve for about 1 h before the sunset.Setting trise and tset as per the ground station plots, ET valueswere estimated at 30 min time step and compared to groundobservations. As an example, Fig. 5 shows the variation of ET(or latent heat flux) over a day as recorded by one ground station(E20), representative of a region of 1 km2 area on ground andimplementation of the sine curve from its observations.Applicability of the proposed sinusoidal model cannot bejudged for days that have cloud cover at any point of time. Foreach selected day, all the ET ground station plots were checkedfor reliability and smoothness of curve to sort out very clear skydays. Any cloud cover or instrument error results in abruptovershooting of the curve or tends to show a zig-zag pattern.Fig. 6 presents the applicability of sine model to ET on theselected station plots for very clear sky days. The bias of15.18 Wm−2, RMSE of 29.39 Wm−2 and R2 of 0.96 suggestthat the proposed sinusoidal model is acceptable to retrievediurnal cycle of ET for very clear sky days. The sinusoidalmodel of Rn is capable of retrieving the diurnal variation of Rn

y = 1.06

0

100

200

300

400

0 100 200

Ground ET (Wm-2

Sin

e M

od

el E

T (

Wm

-2)

Fig. 6. Applicability of sinusoidal mod

with a single INR estimate from satellite (Bisht et al., 2005).Assuming negligible errors, we can estimate soil heat flux as afraction of Rn (Anderson et al., 1997). By estimating the daily ordaytime average available radiant energy (Rn−G), we canestimate the daily or daytime average ET by using instantaneousEF derived from satellite data (Islam et al., 2003; Nishida et al.,2003; Norman et al., 2003).

7. Ground validation of ET

ET estimates from remote sensing are validated to groundestimates at MODIS overpass time since EF remains fairlyconstant during the daytime period, while available energyderived from MODIS data products vary with the time ofoverpass. By checking the reliability of scattered EBBR stationsin SGP for each study day, we do validation as per two sets ofcomparisons given in Sections 5.2 and 5.3. It should be notedthat cloud pixels are masked out from satellite derived ET maps.Satisfying such criteria, we obtained 60 reliable ground pointobservations for validation of ET estimates from MODIS andNOAA16 sensors for the study period given in Section 5.3 and21 ground observations for validation of NOAA14–ET for thestudy period given in Section 5.2. The comparison betweenground measurements and corresponding ET estimates from thethree sensors is shown in Fig. 7 and summarized with the bias,RMSE and R2. The standard error fromMODIS (53 Wm−2) andNOAA16 (51 Wm−2) is of the order of 22% in terms of meanground ET while it is of the order of 28% from NOAA14(56.24 Wm−2). R2 is 0.84, 0.79 and 0.77 from MODIS,NOAA16 and NOAA14 respectively. As discussed in Section 3,there were significant improvements in MODIS and NOAA16sensors as compared to NOAA14 sensor using advancedfeatures to improve the performance. Ground results correlatemuch better with MODIS and NOAA16 sensors, assuring usthat the newly launched sensors hold a great potential for future

77x - 29.382

300 400

)

ET Station

Linear (ET

Station)

N:125

(no. of points)

Bias: 15.18 W/m2

RMSE: 29.39 W/m2

R2 = 0.96

bias:ground -model

el to λET for very clear sky days.

y = 0.6981x + 107.75

0

100

200

300

400

0 100 200 300 400

Observed latent heat flux (W/m2)

Der

ived

late

nt

hea

t fl

ux

(W/m

2) NOAA14

Linear (NOAA14)

Bias: -27.63 Wm-2

RMSE: 55.24 Wm-2

R2 : 0.77

N:21

bias: (obs. - der.)

1:1 Line

yMODIS = 0.7647x + 79.598

yNOAA16 = 0.6707x + 81.911

0

100

200

300

400

0 100 200 300 400

Observed latent heat flux (W/m2)

Der

ived

late

nt

hea

t fl

ux

(W/m

2)

MODIS

NOAA16

Linear (MODIS)

Linear (NOAA16)

R2: 0.84

Bias: -29.45 W/m2RMSE: 53 W/m2

Bias:11.72W/m2RMSE: 51 W/m2

N:60

1:1 line

a

b

R2: 0.79

Fig. 7. Ground validation of λET from multiple sensors for the entire study period. a) Ground validation of λET from MODIS and NOAA16 sensors. b) Groundvalidation of λET from MODIS and NOAA14 sensors.


use. Negative bias suggests an overestimation of as high as29.45 Wm−2 in our derived ET results. The accuracy andsensitivity of the vegetation indices are influenced by sensorcharacteristics, calibration, atmosphere correction, cloud mask,etc. Studies have suggested that poor atmospheric correctionand persistent atmospheric contamination results in over-estimation of vegetation indices from satellite data (Justice etal., 1998; Wang and Woodcock, 2004) which may lead tooverestimation of available radiant energy and also EF.Another key issue is how to average pixel level (1-km pixel)radiation and ET to watershed level radiation and ET. Theinfluence of different aggregation kernels (linear averages,optimal weighting, kriging, land use based weighting, etc.) andtheir impact on validation of net radiation and ET, yet needs tobe explored. Validation of the proposed method over the

watershed scale may provide better results as compared tothose presented here.

We propose a sinusoidal model of ET similar to the model ofRn by Bisht et al. (2005) to retrieve the diurnal cycle of ET forvery clear sky days or days that are not affected by cloud coverat any time of the day. It is practically impossible to get well-distributed cloud free plots over the entire domain and we couldobtain only 100 point estimates from selected stations over theentire study period. By comparing the diurnal cycle of groundET observations and implemented sinusoidal model withMODIS data at an hourly interval, we obtain bias (obs.-derived), RMSE and R2 as −3.75 Wm−2, 73 Wm−2 and 0.5respectively, as shown in Fig. 8. In general, EF being constantover the daylight period, the accuracy of diurnal cycle of ETprimarily depends on the retrieval of diurnal cycle of Rn. Under

y = 0.4471x + 115.6

0

100

200

300

400

0 100 200 300 400

Ground latent heat flux (Wm-2)

Lat

ent

hea

t fl

ux

der

ived

fro

m s

ine

mo

del

(Wm

-2)

MODIS

Linear(MODIS)

N: 100

BIAS : -3.75 W/m2RMSE : 73 W/m2R2 : 0.50

1:1 Line

Fig. 8. Comparison of diurnal cycle of λET and sinusoidal model at an hourly interval for very clear sky days.


cloud cover, incoming solar radiation can be estimated fromgeosynchronous satellites (Brakke & Kanemasu, 1981; Tarpley,1979). Pinker and Laszlo (1992) have proposed a model thatinfers incoming short wave fluxes and surface albedos fromGOES data. Models using GOES data can provide morerealistic continuous diurnal Rn estimation under variable cloudconditions (Pinker et al., 1994). In water stressed areas, ET iscomputed by using a water stress coefficient, Ks, describing theeffect of water stress on crop transpiration which lowers thetranspiration rate. In turn, it may affect the vapor pressure deficitwhich displaces the ET sine pattern (Zhang and Lemeur, 1995).Further research needs to be done to address the applicability ofour sinusoidal model for water stressed conditions.

8. Summary and discussion

Evapotranspiration cannot be measured directly from satelliteobservations but a reasonably good estimate of evaporativefraction (EF) has been obtained by using a contextualinterpretation of radiometric surface temperature and normalizedvegetation index from multiple satellites. Recent studies havesuccessfully estimated net radiation over large heterogeneousareas for clear sky days using only remote sensing observations.With distributed maps of evaporative fraction and net radiation,it is now possible to estimate ET from multiple satellites.

Here we present results of an extensive inter-comparison ofspatially distributed ET and related variables (NDVI, To, EF andRn) derived from MODIS and AVHRR sensors onboard EOSTerra, NOAA14 and NOAA16 satellites respectively. Ourresults show that the obtained NDVI and To values are differentfor the three sensors and are affected by the SRFs and overpasstimes. The differences of about 2–5 K in To are consistent withthe time gap of 2–3 h between overpass time of satellites. Theretrieved NDVI values from AVHRR and NOAA14 areconsistently lower than the retrieved values from MODIS andNOAA16 sensors. The results are as expected in literature

(Trishchenko et al., 2002; Huete et al., 2002; Venturini et al.,2004) explained by the overlapping of red and NIR bands. Itshould be emphasized that the method relies on the contextualdistribution of NDVI and To, and not on the exact values ofthese two parameters. Moreover, the adopted methodology usesa linear relationship between Φ and the normalized temperature,which reduces the influence of absolute values of To induced bylocal atmospheric conditions and sensor viewing angles. It isworthwhile to note that the statistics of Φ estimated fromMODIS and AVHRR sensors are consistent to those of Islam etal. (2003) and Venturini et al. (2004).

A direct comparison between ground measurements andcorresponding satellite results may not always be feasible orrepresentative because of scale-mismatch, geo-location errorsand vegetation heterogeneity at the spatial resolution of satellitedata. To reduce the uncertainties in comparisons betweenkilometer-scale fluxes and ground flux observations, Norman etal. (2003) proposed a high-resolution approach by combininglow (GOES) and high (aircraft) resolution (∼24 m) remotesensing data. Using data from the SGP97 field experiment,RMSE was found to be about 40 Wm−2 between remoteestimates of surface fluxes and ground observations. In ourstudy, ET estimated at 1 km resolution from MODIS, NOAA16and NOAA14 sensors is compared with ground point observa-tions, giving RMSE as 53, 51 and 56.24 Wm−2 respectively.Our results are commendable with respect to the scale ofvalidation used and Norman et al. (2003) results.

It may be noted that this is perhaps the first attempt in whichspatially distributed heterogeneous net radiation map is used inconjunction with a distributed EF map, to determine ET usingonly satellite data. Jiang and Islam (2001) has reported RMSEof 85.3 Wm−2 (29% of mean-ET) by using NOAA–AVHRRdata and interpolated Rn map over SGP in 2001. Nishida et al.(2003) reported bias, RMSE and R2 for ET as 5.59 Wm−2,45.06 Wm−2 and 0.86 respectively from the comparison ofNOAA–AVHRR data to AmeriFlux stations spread over


continental US. They used a linear two-source model of EF bycharacterizing the landscape as a mixture of bare soil andvegetation. Errors from the proposed methodology are compa-rable to those obtained from complex models (Jacobs et al.,2000; Rivas & Caselles, 2004). The ground results correlatemuch better with MODIS and NOAA16, assuring us that thenewly launched sensors hold a great potential for future use.EOS-Aqua satellite, launched in 2002 has an afternoonequatorial crossing time, while EOS-Terra satellite has amorning overpass time. The two satellite missions work intandem with each other and hence use of sensors onboard EOS-Aqua may further improve the accuracy of our ET estimates.Data extraction under cloud cover still remains a challengingtask with the proposed methodology. Even by selecting theclearest satellite images, scattered or shallow cloud contamina-tion remains to be a major issue in the derived ET map.Accuracy of ground flux estimates under cloud cover is alsoquestionable. In recent years, many remote sensors have beenexplored, which show varying degree of success. Thoughstudies are being carried on for the feasibility of usingmicrowave sensors, the spatial resolution of such sensors isvery course for estimating surface ET.

The proposed ET estimation algorithm, primarily driven bysatellite data, appears to be robust and can produce spatiallydistributed ETmap over large heterogeneous areas. Our extensiveinter-comparison of spatially distributed parameters derived fromMODIS and AVHRR sensors and validation of ETover SouthernGreat Plains are promising. We argue that the strength of theproposed ET estimates should be evaluated not only by howclosely they reproduce surface based point observations but bytheir ability to provide a spatially consistent and distributed ETmap over a large heterogeneous domain. The improvement inestimates of ET at 1 km scale over the heterogeneous SGPdomain, despite its shortcomings, is substantial compared tocurrently available spatially distributed ET estimates and the costof maintaining surface based observation networks.

Acknowledgements

This research was supported, in part, by grants from theNational Research Institute Competitive Grants Program(MASR-2004-00888) and the National Aeronautics and SpaceAdministration (NAG5-11694; NNG05M16G) of the UnitedStates of America.

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Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains