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International Journal of Applied Earth Observation and Geoinformation 17 (2012) 8593

Contents lists available at SciVerse ScienceDirect

InternationalJournal ofApplied Earth Observation andGeoinformation

journal homepage: www.elsevier .com/ locate / jag

Estimation ofevapotranspiration in an arid region by remote sensingA case

study in the middle reaches ofthe Heihe River Basin

Xingmin Li a,b,, Ling Lu a, Wenfeng Yang c, Guodong Cheng a

a Cold andArid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences,730000 Lanzhou, Chinab Shaanxi Institute of Meteorological Sciences, 710014 Xian, Chinac Shaanxi Meteorological Observatory, 710014 Xian, China

a r t i c l e i n f o

Article history:

Received 8 November 2010

Accepted 7 September 2011

Keywords:

Heihe River Basin

Evapotranspiration

Remote sensing

Evaporative fraction

a b s t r a c t

Estimating surface evapotranspiration is extremely important for the study of water resources in arid

regions. Data from the National Oceanic and Atmospheric Administrations Advanced Very High Reso-

lution Radiometer (NOAA/AVHRR), meteorological observations and data obtained from the Watershed

Allied Telemetry Experimental Research (WATER) project in 2008 are applied to the evaporative frac-

tion model to estimate evapotranspiration over the Heihe River Basin. The calculation method for the

parameters used in the model and the evapotranspiration estimation results are analyzed and evaluated.

The results observed within the oasis and the banks of the river suggest that more evapotranspiration

occurs in the inland river basin in the arid region from May to September. Evapotranspiration values

for the oasis, where the land surface types and vegetations are highly variable, are relatively small and

heterogeneous. In the Gobi desert and other deserts with little vegetation, evapotranspiration remains

at its lowest level during this period. These results reinforce the conclusion that rational utilization of

water resources in the oasis is essential to manage the water resources in the inland river basin. In the

remote sensing-based evapotranspiration model, the accuracy ofthe parameter estimate directly affects

the accuracy ofthe evapotranspirationresults; more accurate parameter values yield more precise values

for evapotranspiration. However, when using the evaporative fraction to estimate regional evapotranspi-

ration, better calculation results can be achieved only ifevaporative fraction is constant in the daytime.

2011 Elsevier B.V. All rights reserved.

1. Introduction

Evapotranspiration is both a heat balance component and a key

component of the water budget. Because the inputs and outputs

of surface heat and water primarily determine the components

and evolution of the geographic environment, understanding evap-

otranspiration can significantly improve the modeling of energy

balance and water cycle. For research related to global climate

change and land surface processes, evapotranspiration data are

required to calculate water use efficiency, irrigation and water

resource distribution; it is also important for the study of cli-mate change patterns, atmospheric circulation modes, carbon

balance and related land surface processes and boundary condi-

tions (Avissar, 1998; Verstraeten et al., 2005).

For practical applications, it is typically necessary to know both

the distribution of evapotranspiration and the general water con-

sumption trend within the region. Therefore, estimating regional

Corresponding author at: Shaanxi Institute of Meteorological Sciences, 710014

Xian, China. Tel.: +86 29 81619291.

E-mail address: [email protected](X.Li).

evapotranspiration is a key issue. Traditional methods like refer-

ence crop evapotranspiration assume homogeneous land coverage

and structure, but these conditions are difficult to meet for large

regions. In recent years, because of the rapid developments in

remote sensing technology, the spatial, temporal and spectral

resolution of satellite data is continuously improving. Surface

characteristics, such as albedo, vegetation coverage, land surface

temperature, and leaf area index, can be retrieved from visible,

near-infrared, thermal infrared and other wave bands. These data

provide a basis for estimating evapotranspiration from farmland

and other regions and have attracted widespread attention for theuse of remotesensing technologies to study regional evapotranspi-

ration (Li et al., 2009a).

Evapotranspiration with remote sensing technology has been

studied thoroughly in China; for example, the Surface Energy

Balance Algorithm for Land (SEBAL) model (Bastiaanssen et al.,

1998a,b) and the Surface Energy Balance System (SEBS) model (Su,

2002) are used to calculate evapotranspiration. Pang et al. (2007)

developed evapotranspiration estimation models for vegetation

coverage and for bare soil that are based on the SEBAL model.

He et al. (2007) improved the parameters of the SEBS model and

estimated surface energy flux from the topographical features of

0303-2434/$ seefrontmatter 2011 Elsevier B.V. All rightsreserved.

doi:10.1016/j.jag.2011.09.008
http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jag.2011.09.008http://www.sciencedirect.com/science/journal/03032434http://www.elsevier.com/locate/jagmailto:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jag.2011.09.008http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jag.2011.09.008mailto:[email protected]://www.elsevier.com/locate/jaghttp://www.sciencedirect.com/science/journal/03032434http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jag.2011.09.008

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86 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) 8593

the Huang-Huai region. Ma et al. (2010) studied energy flux on a

heterogeneous land surface. Xin and Liu (2010) used assumptions

to simplify the two-source model. Zhang et al. (2003) proposed an

evaporation model based on differential thermal inertia by which

evaporation (latent heat flux) can be determined forbare soil based

on only remotely sensedinformation. This work inspired new ideas

for estimating evapotranspiration by remote sensing techniques,

and integrating these methods with soil-vegetation-atmosphere

models (Liu et al., 2007) is also progressing.

The Heihe River Basin, the second largest inland river basin of

China, is commonly used as a case study area for estimating evap-

otranspiration for a heterogeneous land surface in an arid region.

Many studies have focused on evapotranspiration across the Heihe

RiverBasin. Ma et al.(1997, 2004) implemented a parameterization

scheme for regional energy flux on a heterogeneous land surface

with Landsat TM data and then validated the scheme with obser-

vations from the Heihe River Basin Field Experiment (HEIFE). Guo

(2003) estimated evapotranspiration over the Heihe River Basin

by using NOAA/AVHRR data to retrieve the typical surface parame-

ters basedon PriestleyTaylor formula. Zhang et al.(2004) usedthe

SEBAL model to deduce the spatial distributions of surface energy

fluxesincludingevapotranspirationfrom TM images overthe Heihe

River Basin. In addition, some researchers (Wu et al., 2007) inves-

tigated the receipt and expenditure of radiation by farmland in the

Heihe River Basin based on the reference crop evapotranspiration

method. In contrast, this paper studies and estimates evapotran-

spiration over the midstream of the Heihe River by the evaporative

fraction method and verifies the calculated parameters and model

results by comparing the results with data from the Watershed

Allied Telemetry Experimental Research (WATER) (Li et al., 2009b,

2011).

2. Dataandmethod

2.1. Study area

The middle section of the Heihe River is selected as thestudy area in this paper to estimate evapotranspiration by the

remote sensing model. With an area about 128,000 km2, the Heihe

River Basin is the second largest inland river basin in north-

western China; it is located in the middle of the Hexi Corridor

between 964210200E and 37414242N. The oasis is pri-

marily surrounded by the Gobi desert, but the landscape includes

heterogeneously distributed farmland, forest and residential areas

inside and on the margins of the oasis. The climate is a typical

temperatearid environment with low precipitation andhigh evap-

oration.

2.2. Data

2.2.1. NOAA/AVHRR dataThe Advanced Very High Resolution Radiometer (AVHRR) has

five channels: one visible (VIS), one near-infrared (NIR), one

infrared (IR) and two thermal infrared (TIR) channels. The tem-

poral resolution is one day and the spatial resolution at nadir

is 1.1 km. This paper uses NOAA/AVHRR data received by the

Gausu Provincial Meteorological Bureau on May 5, June 3, August

2 and 25, and September 30, 2008. The overpasses time of satel-

lite NOAA-18 is between 14:00 pm and 16:00 pm local time.

Polar Orbit Meteorological Satellite Receiving and Processing Sys-

tem software, which was developed by the National Satellite

Meteorological Center of the China Meteorological Administra-

tion (CMA), was used to process the received real-time data.

After geometric and atmospheric corrections and other prepro-

cessing such as data format conversion, the data were then

been input into the Environment for Visualizing Images (ENVI)

software (http://www.ittvis.com/ProductServices/ENVI.aspx) for

further analysis.

2.2.2. Meteorological data

Estimating instantaneous evapotranspiration from the

NOAA/AVHRR data requires several types of data, including

air temperature, air pressure, water vapor pressure ( for the

incoming long-wave radiation and short wave transmissivity).These data were obtained from the nine meteorological stations

of Gansu Provincial Meteorological Bureau. The air temperature

observations were interpolated into gridded data with a resolution

of 1.1km, by considering the correlations with latitude, longitude

and altitude. The cross-validation results showed that the interpo-

lation error of temperature derived from the spatial distribution

model is less than 0.7 C in terms of the absolute error. The spatial

interpolation of air pressure was also performed by establishing

a correlation equation between air pressure and altitude. The

absolute error is less than 10Hpa. The water vapor pressure was

interpolated using Kriging.

Observation dataincluding surface albedo, land surface temper-

ature (LST), net radiation, atmospheric long-wave radiation and

eddy correlations were collected at two sites from the WATERexperiment in 2008 (Fig. 1) for validation of the model. One is the

Yingke site and the other is the Huazhaizi site. The landscapes in

thesetwo sitesare farmlandand desert steppe, respectively. Details

of these sites can be found in Li et al. (2009b, 2011).

2.2.3. Other data

DEM data and the land use map of the Heihe River Basin were

obtained from the Environmental and Ecological Science Data in

western China (http://westdc.westgis.ac.cn).

2.3. Evaporative fraction model

The evaporative fraction is defined as follows:

=E

E+ H =

E

Rn G0(1)

According to the surface energy balance equation,

E=RnG0 H, Ecan be expressed as follows:

E= (Rn G0) (2)

where E is the latent heat flux (Wm2), Rn is the net radiation

flux (net short and net long-wave) (Wm2), G0is the soil heat flux

(W m2)andHisthesensibleheatflux(Wm2).Latentheatfluxcan

be calculated from the net radiation, soil heat flux and evaporative

fraction.

Therefore, evapotranspiration can be assessed from remotesensing image by estimating the evaporative fraction, net surface

radiation and the soil heat flux.

2.3.1. Determining the net radiation

Net radiation is determined by:

Rn = (1 0)K +0 L 0 LST40 (3)

where Rn is net radiation (Wm2), 0 is surface albedo (), K

is incoming short wave radiation (Wm2), L is incoming long-

wave radiation (Wm2), 0 is surface emissivity (), is the

StefanBoltzmann constant as 5.67108 W m2 K4 and LST0 is

land surface temperature (K). The estimation of these parameters

is detailed below.
http://www.ittvis.com/ProductServices/ENVI.aspxhttp://westdc.westgis.ac.cn/http://westdc.westgis.ac.cn/http://www.ittvis.com/ProductServices/ENVI.aspx

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X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) 8593 87

Fig. 1. Observation sites in themiddle reaches of theHeiheRiverBasin.

2.3.2. Solar short wave radiationThe instantaneous daily incoming short wave solar radiation on

a surface for clear sky conditions is expressed as:

K= I0E0 cos() (4)

where I0 is the instantaneous extraterrestrial solar radiation

(1367Wm2) (Iqbal, 1983), E0 is an eccentricity correction factor

(Iqbal, 1983), is solar zenith angle (Wu et al., 1995), and is the

transmittance in the short wave broadband range () (Iqbal, 1983).

2.3.3. Surface albedo

The broadband albedo is calculated from a simple linear combi-

nation of data from channels one (red) and two (NIR) of the AVHRR

sensor. Guo (2003) and Song and Gao (1999) introduced the fol-lowing formula to calculate the surface albedo:

0 = 0.545vis+ 0.32nir+ 0.035 (5)

where vis and nir are the visible and near-infrared reflectance

from NOAA/AVHRR data, respectively.

2.3.4. Incoming long-wave radiation

The approach implemented here estimates instantaneous

incoming long-wave radiation by the method describedby Iziomon

et al. (2003):

L =

1 a exp

b

e0T

air

T4air (6)

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wherea= 0.35,b=10.0KhPa1, is the StefanBoltzmann constant

and Tairis the air temperature (K).

2.3.5. Outgoing long-wave radiation

The StefanBoltzmann law is used to calculate the long-wave

radiation from land surface:

L0 = 0 LST40 (7)

2.3.6. Surface emissivity

Surface emissivity is estimatedby themethod of thenormalized

differencevegetation index(NDVI) threshold,which is describedby

Sobrino and Raissouni (2000).

2.3.7. Land Surface temperature

Land surface temperature is extracted from the brightness tem-

peratures recorded by channels four and five of the NOAA/AVHRR

instrument using the split window technique described by Ulivieri

et al. (1994):

LST0 = 2.8T4 1.8T5+ 48(1 0) 75 (8)

where T4 and T5 are the brightness temperatures in channels fourand five, respectively. is the difference between the surface

emissivity in channel four and the surface emissivity in channel

five.

2.3.8. Determination of soil heat flux

Bastiaanssen et al. (1998a) utilizes a proportionality factor that

describes conductive heat transfer in the soil using LST0, albedo

(0) and an extinction factor that describes the reflection of radia-

tion through vegetation canopies based on NDVI according to the

following formula:

G0 = Rn

LST0 273.15

0(0.0032C10+ 0.0062C

21

20)(1

0.978NDVI4)

(9)

where C1is a conversion factor to obtain the daily average surface

albedo from instantaneous values (default= 1.1) ().

2.3.9. Determination of the evaporative fraction

The major assumption in the NOAA/AVHRR data set is that

the evaporative fraction remains constant for all overpass times

associated with a daily satellite image (Crago, 1996; Franks and

Beven, 1997). This assumption holds for environmental conditions

under which soil moisture does not change significantly. Changes

in weather conditions or cloud cover and surface discontinuities

can induce significant variability of the evaporative fraction.

A simple method to approximate the evaporative fraction is

to combine albedo with land surface temperature as described by

Su and Menenti (1999), Su et al. (1999), Roerink et al. (2000) and

Verstraeten et al. (2005).

=E

E+ H

LSTH LST0LSTH LSTE

H 0 + bH LST0

(H E)0 + (bH bE) (10)

where LSTHis the land surface temperature for drypixels (K); LSTEisthe land surface temperaturefor wetpixels(K); Hand Earethe

slopesof thehigh andlow surface temperature,respectively, which

are functions of surface albedo (K); bHand bEare the intercepts of

the high and low temperature, respectively, which are functions of

surface albedo (K). By fitting the linear equations for the upper and

lower boundaries ofLST0 0, the slope and intercept of Eq. (10)

can be obtained.

Fig. 2. The scatter plot of NDVI andevapotranspirationat themiddle reaches of the

Heihe River Basin on June 3, 2008.

3. Evaluation

3.1. Evaluation of surface physical parameters

3.1.1. Evaluation of surface albedo

Table 1 compares the observed surface albedo at the Yingke andHuazhaizievaluation siteswith the surface albedo calculatedby Eq.

(5). With the exception of the surface albedo at the Yingke oasis on

June 3, 2008, the maximum absolute error is 0.037, and the mini-

mum absolute error is 0.009.These results suggest that themethod

for estimating the surface albedo has small errors.

3.1.2. Evaluation of outgoing long-wave radiation

Table 2 compares the observed outgoing long-wave radiation at

the Yingke andHuazhaizi sites to theoutgoinglong-waveradiation

calculated by Eq. (7). The maximum absolute error of the outgoing

long-wave radiation is 39.2 W m2 (relative error of 7.9%), and the

minimum absolute error is only 1.3W m2 (relative error of 0.38%).

Therefore, the methods used to estimate land surface temperature

and outgoing long-wave radiation show effective.

3.1.3. Evaluation of incoming long-wave radiation

Table 3 compares the observed incoming long-wave radiation

at Yingke and Huazhaizi to the incoming long-wave radiation cal-

culated from Eq. (6). As shown in this table, the maximum absolute

error between theobservedand calculated values is 16.2 W m2 (at

Yingke), andthe corresponding relative error is 5.2%; theminimum

absolute error is 4.1W m2 (at Huazhaizi), and the correspond-

ing relative error is only 1.2%. Therefore, the calculated results

for incoming long-wave radiation show a high accuracy for both

farmland and grassland sites.

3.1.4. Evaluation of net radiationTable 4 compares the observed net surface radiation at Yingke

and Huazhaizi to the estimated net surface radiation from Eq. (3).

Themaximum absolute error is 87W m2 atYingkeon June 3,2008,

andits relative error is 14.1%. Based on the results in Tables 1 and 2,

errors in the estimated surface albedo and outgoing long-wave

radiationlead to erroneousnet radiationvaluesfor thatday because

reflected radiation and outgoing long-wave radiation are used to

estimate net radiation. At Yingke, the relative errors are less than

10% on August 2 and September 30.

At Huazhaizi, the relative errorin net radiationis 12.1% (absolute

error of 42.6W m2) on June 3, 2008. Previous results suggest that

this error may have been due to errors in the estimated outgoing

long-wave radiation and incoming long-wave radiation. On August

25, 2008, the relative error in the net radiation of 11.2% may have

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Table 1

Comparison between the measurements and the calculated results of land surface albedo.

Date Observation sites Measured value Calculated value Absolute error Relative error (%)

May 5 Huazhaizi

Yingke 0.219 0.229 0.010 4.6

June 3 Huazhaizi 0.274 0.265 0.009 3.3

Yingke 0.109 0.188 0.078 71.5

August 2 Huazhaizi 0.249 0.239 0.010 4.0

Yingke 0.148 0.128 0.021 14.2

August 25 Huazhaizi 0.265 0.228 0.037 14.0Yingke

September 30 Huazhaizi 0.166 0.174 0.008 4.8

Yingke 0.147 0.157 0.010 6.8

Table 2

Comparison between the measurements and the calculated results of outgoing long-wave radiation.

Date Yingke Huazhaizi

Measured

value(W m2)

Calculated

value(W m2)

Absolute error

(W m2)

Relative

error (%)

Measured

value(W m2)

Calculated

value(W m2)

Absolute error

(W m2)

Relative

error (%)

May 5 N/A 465.5

June 3 495.7 535.0 39.2 7.9 613.5 595.6 17.9 2.9

August 2 466.3 477.6 11.3 2.4 550.5 544.0 6.5 1.2

August 25 573.2 564.2 9.0 1.6

September 30 474.0 454.8 19.2 4.1 460.1 461.4 1.3 0.4

Table 3

Comparison between the measurements and the calculated results of incoming long-wave radiation.


Measured

value(W m2)

Calculated

value(W m2)

Absolute error

(W m2)

Relative

error (%)

Measured

value(W m2)

Calculated

value(W m2)

Absolute error

(W m2)

Relative

error (%)

June 3 351.4 346.4 5.0 1.4 332.5 336.6 4.1 1.2

August 2 353.2 364.0 10.8 3.1 339.8 352.4 12.6 3.7

August 25 346.3 336.2 10.1 2.9

September 30 311.1 327.3 16.2 5.2 313.5 318.3 4.8 1.5

been due to errors in estimated surface albedo. The relative error

is less than 10% on the other two days.

The evaluation of retrieval results of the surface parametersdemonstrates that errors in the estimated physical surface param-

eters are small in the evaporative fraction model. The results

therefore reasonably reflect the characteristics of the regional

energy balance and the distribution of biophysical parameters.

3.2. Evaluation of evapotranspiration

The observed evapotranspiration at Yingke is compared to the

estimated evapotranspiration by evaporative fraction model and

displayedhere (Table 5). The maximum relative errorbetween June

andAugust, when vegetative cover is high, ranges from 1% to 13.2%.

Because the evapotranspiration estimation model involves many

parameters, errors in each estimated parameter can affect the final

evapotranspiration result. For August 2, errors in the estimated val-

ues for surface albedo, land surface temperature (LST), outgoing

long-wave radiation and incoming long-wave radiation were rel-

atively small; thus, the error in the estimated evapotranspiration

was also small. Therefore, the best evapotranspiration results can

be achieved by applying the best possible methods to obtain

each parameter in the evaporative fraction model. More accurateestimated parameters yield more accurate estimates of evapotran-

spiration.

In early May and late September, estimates of evapotranspira-

tion tend to have larger errors due to sparse vegetation or a lack

of contrast between vegetative coverage inside and outside of the

oasis. In this case, the evapotranspiration model is less able to pro-

duce meaningful results.

4. Results and analysis

4.1. Spatial variation of evapotranspiration in themiddle reaches

of the Heihe River Basin

A comparison of the calculated results of evapotranspiration

with the NDVI indicates a strong relationship over the study area.

Fig. 2 is a scatter plot of NDVI and latent heat flux at the middle

reaches of the Heihe River Basin on June 3, 2008, showing almost a

Table 4

Comparison between the measurements and the calculated results of net radiation.


Measured

value(W m2)

Calculated

value(W m2)

Absolute error

(W m2)

Relative

error (%)

Measured

value(W m2)

Calculated

value(W m2)

Absolute error

(W m2)

Relative

error (%)

June 3 617.2 530.0 87.2 14.1 351.4 394.0 42.6 12.1

August 2 659.2 646.0 13.2 2.0 480.0 498.0 18.0 3.8

August 25 398.5 443.0 44.5 11.2

September 30 438.5 473.0 34.5 7.8 458.7 451.6 7.1 1.5

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Fig. 3. Distribution of latent heat flux over central Heihe River Basin in 2008 (a, May 5; b, June 3; c, August 2; d, September 30).

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Table 5

Comparison between themeasurements and thecalculated results of latentheat flux at Yingke.

Date Measured value (W m2) Calculated value (W m2) Absolute error (W m2) Relative error (%)

May 5 158.6 209.0 50.4 31.8

June 3 372.8 323.0 49.8 13.3

August 2 437.0 485.0 48.0 11.0

August 25 523.9 519.0 4.9 0.9

September 30 150.8 265.0 114.2 75.7

linear positive correlation between NDVI and evapotranspiration.

But it is also clear that at low NDVI values there are a lot of outliers.

On May 5,2008 (Fig. 3a), the maximum value of latent heat flux

(300350W m2) occurs in parts of Gaotai, which is located on the

bank of the Heihe River bank and scattered across some areas of

Jiuquan oasis. The next highest values, 200300W m2, are found

in Zhangye, Linze, Gaotai, most part of the Jiuquan oasis and on

the banks of the Heihe River. Evapotranspiration values in the Gobi

desert are obviously low, typically less than 50W m2. Areas along

the moisture river banks with more vegetation coverage get more

evapotranspiration than other regions. In many artificial oases of

the middle reaches of the Heihe River in early May, spring wheat

is just in the tiller or jointing stage and spring corn is in the trefoil

stage. Because the vegetation index is relatively low at this point,

there is no pronounced difference in evapotranspiration between

the oases and the surrounding desert areas.

On June 3, 2008 (Fig. 3b), the maximum value of latent heat

flux is seen within the oases at Zhangye, Linze, Gaotai and Jiuquan,

where it reaches from 450 to 500 W m2; the latent heat flux

and NDVI in most other parts of the oases are between 350 and

450Wm2 and between 0.3 and 0.4, respectively. At the edge of

the oases, where vegetation is sparse, the latent heat flux ranges

from about 350400W m2. The NDVIof the Jinta oasis isless than

0.2, and its latent heat flux is also smaller (200300W m2).In the

Gobi desert, just outside the oases, the latent heat flux is below

150Wm2. Because almost no plants grow in the desert, the latent

heat flux sharply decreases to a dozen W m2 in some places.

With vegetationgrowth in August, themaximum value of latent

heat flux(500550 W m2) occurson August2, 2008at theZhangyeoasis and Linze oasis, which lies on the Heihe River banks (Fig. 3c).

In Gaotai, Linze and the northern part of the Zhangye, the latent

heat flux is between 400 and 500 W m2. The latent heat flux

is 350400W m2 across most of the Jiuquan oasis and is about

200400W m2 in Jinta oasis and NDVI is about 0.10.4; these

values indicate thatthe landscapewithin the Jintaoasis is heteroge-

neous and that the vegetation distribution varies greatly. However,

the latent heat flux in the Gobi desert area varies much less in

August than in June.

On September 30, 2008 (Fig. 3d), the green of the NDVI image

rapidly declines, which corresponds to crop maturity and harvest.

The maximum value of latent heat flux is again found in Zhangye

oasis with a value between 300 and 350W m2. In this oasis, the

large value of the NDVI (0.30.4) indicates that some plants arestillgrowing. Higher values of the latent heat flux are also scattered

across some areas of Linze, Gaotai and Jiuquan at this time, but the

latent heat flux in most parts of the oases drops to between 250

and 300Wm2 from more than 350W m2 in August. In the Gobi

desert, which has little vegetation, evapotranspiration remains at

a steady low level.

4.2. Temporal variation of evapotranspiration in the central

Heihe River

Based on the distribution of evapotranspiration values on May

5, June 3, August 2 and September 30, during all of these months

evapotranspirationis very lowin theGobi desert, where vegetation

issparse.Withtheexceptionofafewplacesneartheoasesthathave

slightly more evapotranspiration, the latent heat flux is typically

less than 10Wm2 in some places. This condition is the result of

the surface energy balancing process, in which sensible heat flux

and soil heat flux are major parameters, whereas latent heat flux

has minimal influence.

Evapotranspiration variation in the middle reaches of the Heihe

River Basin is closely related to crop growth. In early May, when

springwheatis in thetiller or jointing stage andcorn is in thetrefoil

stage, the ground has limited vegetation cover, and evapotranspi-

ration values are similar for the oasis and the surrounding desert.

On August 2, when spring wheat, maize and other crops are expe-

riencing vigorous growth, oasis evapotranspiration increases to a

maximum value. Harvesting at the end of September causes oasis

evapotranspirationto become lower than that in August. Both river

banks are distinguished from the surrounding desert by their rel-

atively moist conditions and high evapotranspiration values on all

of the studied dates. Changes in the evapotranspiration in desert

areas are consistently small.

Evapotranspirationvariations in the middle reaches of the Heihe

River Basin are closely related to crop growth and it has a strong

relationship with the NDVI. This suggests that according to the

characteristics of spatial and temporal variation of evapotranspira-

tion, rational utilization of water resources in the oasis is essential

to manage the water resources in the inland river basin.

5. Discussion

Estimated evapotranspiration values are evaluated by compari-

son to surface flux observations from WATER. During months with

greater vegetative cover (June, July and August), the relative error

in the results of the evaporative fraction model ranges from 13.2%

to as little as 1%. For a pixel range of 1.1km, this result is accept-

able. However, when vegetation cover in the middle reaches of the

Heihe River Basin is sparse (May and September), the evapotran-

spiration estimation error is relatively large, which indicates that

the method is not effective for the condition with less vegetative

coverage.

Changes in the evaporative fraction at Yingke are evaluated

based on data from May 5, September 30, June 3 and August 2 and

25, 2008 (Fig. 4). In the early stage of crop growth (May 5) when

Fig. 4. Daily variations of the evaporative fraction at Yingke site.

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vegetative coverage is low and the NDVI is 0.06, the change in the

evaporative fraction is obvious. In the late stage of crop growth

(September 30), the NDVI is 0.21, and the evaporative fraction also

changes clearly during the day. During the vigorous growth stage

(June 3, August 2 and 25), the NDVI is above 0.30, and the evap-

orative fraction changes little during the day. Therefore, changes

in the evaporative fraction likely contribute to the errors in esti-

mated evapotranspiration. In addition, satellite passing time and

observation time are not completely consistent, which generates

the difference between the observed and estimated values. For

instance, on May 5, at the satellite passing time of 15:40 (local

time), the estimated latent heat flux was 209W m2; the latent

heat flux values observed on the ground were 203.3 W m2 at

15:15 and 158.6 Wm2 at 15:45. On September 30, at the satel-

lite passing time of 15:00, the estimated latent heat flux was

265Wm2; observed latent heat flux values were 150.9 W m2 at

15:15 and 167.7 Wm2 at 15:45. Therefore, the evaporative frac-

tion changes during the early and late crop growth stages, and the

timeassociatedwith satellite and ground-based measurements are

not completely consistent. These factors contribute greatly to the

large estimation errors on May 5 and September 30.

Two major error sources should be discussed here. One is the

error related with the temporal scale. The estimated evapotran-

spiration extracted from remote sensing data is an instantaneous

value, while the evapotranspiration measured by the Eddy Covari-

ance system(EC) is generallya mean value withina certain average

period (normally 30min). Therefore, the difference in the tempo-

ral scale might make some contribution to the errors between the

estimated and measured evapotranspiration.

The other is the error related with the spatial scale. EC at the

Yingke station is set up at 28m high to observe evapotranspiration

of cropland. Its footprint is about 250m of radius in circle range

according to the study ofShang et al. (2009). When comparing the

latent heat flux value measured by EC with the estimated value

from NOAA/AVHRR data with 1.1 km resolution, the difference in

spatial scale may bring errors (Song, 2011). However, how to take

account the scale issue into validation is a very challenge issue and

it needs further investigation. It believes that higher spatial resolu-tion remote sensing data can play an important role in solving this

problem.

The model is also sensitive to theerrors in the input parameters,

to quantify these errors is also of great importance. To incorporate

the remote sensing model of evapotranspiration into a land data

assimilation system for better estimation of ET and quantification

related errors will be our future research priority.

6. Conclusion

This paper uses the remote sensing data and WATER observa-

tions to estimate the evapotranspiration over the middle reaches

of the Heihe River Basin based on the evaporative fraction method.The evaluation of the obtained parameters and modeling results

suggest that the method produces only small errors and is able

to achieve the goals of a good ET estimation. When estimating

regional evapotranspiration from the evaporative fraction, vege-

tative growth conditions must be considered. Good results can be

obtained only if the evaporative fraction remains constant during

the day. Generally, during peak crop growth (June to August), the

evaporative fraction model can yield accurate results for evapo-

transpiration.

Evapotranspiration and NDVI distribution are strongly corre-

lated. The oases and river banks, which have higher NDVI, typically

have greater evapotranspiration, whereas areas with sparse or no

vegetation coverage normally have very low evapotranspiration.

There are also some exceptions in Fig. 2. We found that most of the

points apart from the linear correlation line are distributed near

the river. They have little vegetation cover, with NDVI value

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