International Journal of Applied Earth Observation and Geoinformation

Transcription

1 International Journal of Applied Earth Observation and Geoinformation 17 (2012) Contents lists available at SciVerse ScienceDirect International Journal of Applied Earth Observation and Geoinformation jo u r n al hom epage: Estimation of evapotranspiration in an arid region by remote sensing A case study in the middle reaches of the Heihe River Basin Xingmin Li a,b,, Ling Lu a, Wenfeng Yang c, Guodong Cheng a a Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China b Shaanxi Institute of Meteorological Sciences, Xi an, China c Shaanxi Meteorological Observatory, Xi an, 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 Administration s Advanced Very High Resolution Radiometer (NOAA/AVHRR), meteorological observations and data obtained from the Watershed Allied Telemetry Experimental Research (WATER) project in 2008 are applied to the evaporative fraction 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 of the parameter estimate directly affects the accuracy of the evapotranspiration results; more accurate parameter values yield more precise values for evapotranspiration. However, when using the evaporative fraction to estimate regional evapotranspiration, better calculation results can be achieved only if evaporative fraction is constant in the daytime 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 evapotranspiration 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 climate change patterns, atmospheric circulation modes, carbon balance and related land surface processes and boundary conditions (Avissar, 1998; Verstraeten et al., 2005). For practical applications, it is typically necessary to know both the distribution of evapotranspiration and the general water consumption trend within the region. Therefore, estimating regional Corresponding author at: Shaanxi Institute of Meteorological Sciences, Xi an, China. Tel.: address: lixingmin803@163.com (X. Li). evapotranspiration is a key issue. Traditional methods like reference 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 the use of remote sensing technologies to study regional evapotranspiration (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 /$ see front matter 2011 Elsevier B.V. All rights reserved. doi: /j.jag

2 86 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) 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 for bare soil based on only remotely sensed information. 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 evapotranspiration for a heterogeneous land surface in an arid region. Many studies have focused on evapotranspiration across the Heihe River Basin. 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 observations 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 parameters based on Priestley Taylor formula. Zhang et al. (2004) used the SEBAL model to deduce the spatial distributions of surface energy fluxes including evapotranspiration from TM images over the Heihe River Basin. In addition, some researchers (Wu et al., 2007) investigated 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 evapotranspiration 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. Data and method 2.1. Study area The middle section of the Heihe River is selected as the study area in this paper to estimate evapotranspiration by the remote sensing model. With an area about 128,000 km 2, the Heihe River Basin is the second largest inland river basin in northwestern China; it is located in the middle of the Hexi Corridor between E and N. The oasis is primarily 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 temperate arid environment with low precipitation and high evaporation Data NOAA/AVHRR data The 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 temporal 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, The overpasses time of satellite NOAA-18 is between 14:00 pm and 16:00 pm local time. Polar Orbit Meteorological Satellite Receiving and Processing System software, which was developed by the National Satellite Meteorological Center of the China Meteorological Administration (CMA), was used to process the received real-time data. After geometric and atmospheric corrections and other preprocessing such as data format conversion, the data were then been input into the Environment for Visualizing Images (ENVI) software ( for further analysis 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 1.1 km, by considering the correlations with latitude, longitude and altitude. The cross-validation results showed that the interpolation 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 10 Hpa. The water vapor pressure was interpolated using Kriging. Observation data including surface albedo, land surface temperature (LST), net radiation, atmospheric long-wave radiation and eddy correlations were collected at two sites from the WATER experiment in 2008 (Fig. 1) for validation of the model. One is the Yingke site and the other is the Huazhaizi site. The landscapes in these two sites are farmland and desert steppe, respectively. Details of these sites can be found in Li et al. (2009b, 2011) 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 ( Evaporative fraction model The evaporative fraction is defined as follows: = E E + H = E R n G 0 (1) According to the surface energy balance equation, E = R n G 0 H, E can be expressed as follows: E = (R n G 0 ) (2) where E is the latent heat flux, R n is the net radiation flux (net short and net long-wave), G 0 is the soil heat flux and H is the sensible heat flux. Latent heat flux can be calculated from the net radiation, soil heat flux and evaporative fraction. Therefore, evapotranspiration can be assessed from remote sensing image by estimating the evaporative fraction, net surface radiation and the soil heat flux Determining the net radiation Net radiation is determined by: R n = (1 0)K +ε 0 L ε 0 LST 4 0 (3) where R n is net radiation, 0 is surface albedo ( ), K is incoming short wave radiation, L is incoming longwave radiation, ε 0 is surface emissivity ( ), is the Stefan Boltzmann constant as W m 2 K 4 and LST 0 is land surface temperature (K). The estimation of these parameters is detailed below.

3 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) Fig. 1. Observation sites in the middle reaches of the Heihe River Basin Solar short wave radiation The instantaneous daily incoming short wave solar radiation on a surface for clear sky conditions is expressed as: K = I 0 E 0 cos() (4) where I 0 is the instantaneous extraterrestrial solar radiation (1367 W m 2 ) (Iqbal, 1983), E 0 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) Surface albedo The broadband albedo is calculated from a simple linear combination of data from channels one (red) and two (NIR) of the AVHRR sensor. Guo (2003) and Song and Gao (1999) introduced the following formula to calculate the surface albedo: 0 = vis nir (5) where vis and nir are the visible and near-infrared reflectance from NOAA/AVHRR data, respectively Incoming long-wave radiation The approach implemented here estimates instantaneous incoming long-wave radiation by the method described by Iziomon et al. (2003): L = [ 1 a exp ( b e 0 T air )] T 4 air (6)

4 88 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) where a = 0.35, b = 10.0 Kh Pa 1, is the Stefan Boltzmann constant and T air is the air temperature (K) Outgoing long-wave radiation The Stefan Boltzmann law is used to calculate the long-wave radiation from land surface: L 0 = ε 0 LST 4 0 (7) Surface emissivity Surface emissivity is estimated by the method of the normalized difference vegetation index (NDVI) threshold, which is described by Sobrino and Raissouni (2000) Land Surface temperature Land surface temperature is extracted from the brightness temperatures recorded by channels four and five of the NOAA/AVHRR instrument using the split window technique described by Ulivieri et al. (1994): LST 0 = 2.8T 4 1.8T (1 ε 0 ) 75 ε (8) where T 4 and T 5 are the brightness temperatures in channels four and five, respectively. ε is the difference between the surface emissivity in channel four and the surface emissivity in channel five Determination of soil heat flux Bastiaanssen et al. (1998a) utilizes a proportionality factor that describes conductive heat transfer in the soil using LST 0, albedo ( 0) and an extinction factor that describes the reflection of radiation through vegetation canopies based on NDVI according to the following formula: [ LST G 0 = R n (0.0032C C )(1 0 ] 0.978NDVI 4 ) (9) where C 1 is a conversion factor to obtain the daily average surface albedo from instantaneous values (default = 1.1) ( ) 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 LST H LST 0 H 0 + b H LST 0 LST H LST E ( H E ) 0 + (b H b E ) (10) where LST H is the land surface temperature for dry pixels (K); LST E is the land surface temperature for wet pixels (K); H and E are the slopes of the high and low surface temperature, respectively, which are functions of surface albedo (K); b H and b E are 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 of LST 0 0, the slope and intercept of Eq. (10) can be obtained. Fig. 2. The scatter plot of NDVI and evapotranspiration at the middle reaches of the Heihe River Basin on June 3, Evaluation 3.1. Evaluation of surface physical parameters Evaluation of surface albedo Table 1 compares the observed surface albedo at the Yingke and Huazhaizi evaluation sites with the surface albedo calculated by 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 minimum absolute error is These results suggest that the method for estimating the surface albedo has small errors Evaluation of outgoing long-wave radiation Table 2 compares the observed outgoing long-wave radiation at the Yingke and Huazhaizi sites to the outgoing long-wave radiation calculated by Eq. (7). The maximum absolute error of the outgoing long-wave radiation is 39.2 W m 2 (relative error of 7.9%), and the minimum absolute error is only 1.3 W m 2 (relative error of 0.38%). Therefore, the methods used to estimate land surface temperature and outgoing long-wave radiation show effective 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 calculated from Eq. (6). As shown in this table, the maximum absolute error between the observed and calculated values is 16.2 W m 2 (at Yingke), and the corresponding relative error is 5.2%; the minimum absolute error is 4.1 W m 2 (at Huazhaizi), and the corresponding 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 Evaluation of net radiation Table 4 compares the observed net surface radiation at Yingke and Huazhaizi to the estimated net surface radiation from Eq. (3). The maximum absolute error is 87 W m 2 at Yingke on June 3, 2008, and its relative error is 14.1%. Based on the results in Tables 1 and 2, errors in the estimated surface albedo and outgoing long-wave radiation lead to erroneous net radiation values for that day 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 error in net radiation is 12.1% (absolute error of 42.6 W m 2 ) on June 3, 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

5 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) Table 1 Comparison between the measurements and the calculated results of land surface albedo. Date Observation sites value value May 5 Huazhaizi Yingke June 3 Huazhaizi Yingke August 2 Huazhaizi Yingke August 25 Huazhaizi Yingke September 30 Huazhaizi Yingke Table 2 Comparison between the measurements and the calculated results of outgoing long-wave radiation. Date Yingke Huazhaizi May 5 N/A June August August September Table 3 Comparison between the measurements and the calculated results of incoming long-wave radiation. Date Yingke Huazhaizi June August August September 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 parameters demonstrates that errors in the estimated physical surface parameters 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 Evaluation of evapotranspiration The observed evapotranspiration at Yingke is compared to the estimated evapotranspiration by evaporative fraction model and displayed here (Table 5). The maximum relative error between June and August, 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 values for surface albedo, land surface temperature (LST), outgoing long-wave radiation and incoming long-wave radiation were relatively 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 accurate estimated parameters yield more accurate estimates of evapotranspiration. In early May and late September, estimates of evapotranspiration 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 produce meaningful results. 4. Results and analysis 4.1. Spatial variation of evapotranspiration in the middle 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. Date Yingke Huazhaizi June August August September

6 90 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) 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).

7 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) Table 5 Comparison between the measurements and the calculated results of latent heat flux at Yingke. Date May June August August September 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 ( W m 2 ) 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, W m 2, 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 50 W m 2. 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 m 2 ; the latent heat flux and NDVI in most other parts of the oases are between 350 and 450 W m 2 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 W m 2. The NDVI of the Jinta oasis is less than 0.2, and its latent heat flux is also smaller ( W m 2 ). In the Gobi desert, just outside the oases, the latent heat flux is below 150 W m 2. Because almost no plants grow in the desert, the latent heat flux sharply decreases to a dozen W m 2 in some places. With vegetation growth in August, the maximum value of latent heat flux ( W m 2 ) occurs on August 2, 2008 at the Zhangye oasis 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 m 2. The latent heat flux is W m 2 across most of the Jiuquan oasis and is about W m 2 in Jinta oasis and NDVI is about ; these values indicate that the landscape within the Jinta oasis is heterogeneous 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 350 W m 2. In this oasis, the large value of the NDVI ( ) indicates that some plants are still growing. 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 300 W m 2 from more than 350 W m 2 in August. In the Gobi desert, which has little vegetation, evapotranspiration remains at a steady low level. is sparse. With the exception of a few places near the oases that have slightly more evapotranspiration, the latent heat flux is typically less than 10 W m 2 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 spring wheat is in the tiller or jointing stage and corn is in the trefoil stage, the ground has limited vegetation cover, and evapotranspiration values are similar for the oasis and the surrounding desert. On August 2, when spring wheat, maize and other crops are experiencing vigorous growth, oasis evapotranspiration increases to a maximum value. Harvesting at the end of September causes oasis evapotranspiration to become lower than that in August. Both river banks are distinguished from the surrounding desert by their relatively moist conditions and high evapotranspiration values on all of the studied dates. Changes in the evapotranspiration in desert areas are consistently small. Evapotranspiration variations 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 evapotranspiration, 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 comparison 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 1.1 km, this result is acceptable. However, when vegetation cover in the middle reaches of the Heihe River Basin is sparse (May and September), the evapotranspiration 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 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 evapotranspiration is very low in the Gobi desert, where vegetation Fig. 4. Daily variations of the evaporative fraction at Yingke site.

8 92 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) 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 evaporative fraction changes little during the day. Therefore, changes in the evaporative fraction likely contribute to the errors in estimated 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 209 W m 2 ; the latent heat flux values observed on the ground were W m 2 at 15:15 and W m 2 at 15:45. On September 30, at the satellite passing time of 15:00, the estimated latent heat flux was 265 W m 2 ; observed latent heat flux values were W m 2 at 15:15 and W m 2 at 15:45. Therefore, the evaporative fraction changes during the early and late crop growth stages, and the time associated with 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 evapotranspiration extracted from remote sensing data is an instantaneous value, while the evapotranspiration measured by the Eddy Covariance system (EC) is generally a mean value within a certain average period (normally 30 min). Therefore, the difference in the temporal 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 28 m high to observe evapotranspiration of cropland. Its footprint is about 250 m of radius in circle range according to the study of Shang 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 resolution remote sensing data can play an important role in solving this problem. The model is also sensitive to the errors 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 observations 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, vegetative 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 evapotranspiration. Evapotranspiration and NDVI distribution are strongly correlated. 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 <0.02, but have slightly high latent heat flux due to higher soil moisture along the river side. Therefore, different soil moisture conditions at locations with similar low vegetation cover influences the relationship between latent heat flux and NDVI. Other places with low NDVI and relatively higher latent heat flux are located around the cloud cover area. Subpixel cloud generally has low NDVI and affects the estimation of evapotranspiration. Evapotranspiration change in the middle reaches of the Heihe River Basin is closely related to crop growth. The distribution map for August 2 shows maximum evaporation values throughout the oases. During the harvest at the end of September, oasis evapotranspiration is notably lower than in August. Evapotranspiration values for the oases, where the distribution of vegetation is highly variable, are relatively heterogeneous. The two banks of the river are distinguished from the surrounding desert by their higher moisture level and resulting higher evapotranspiration. Few plants grow in the desert, and evapotranspiration is consistently low. With the exception of a few places near the oases that have slightly higher evapotranspiration, the evapotranspiration is generally less than 10 W m 2 in these areas, suggesting that the latent heat flux does not make a significant contribution to the surface energy balance in desert area. The accuracy of the collected data used in the evapotranspiration estimation model directly affects the accuracy of the estimated evapotranspiration. The model involves many parameters, and errors in each parameter can affect the calculated results. Because the data for August 2 have relatively low errors, the estimated evapotranspiration is also relatively better. Therefore, to achieve the best evapotranspiration result, the best available methods must be used to estimate every parameter involved in the evaporative fraction model. Acknowledgements This study was supported by the National Natural Science Foundation of China under Grant Nos and , the National Key Technologies R&D program of China under Grant No. 2009AA12Z1463 and the Research Fund of State Key Lab of Resources and Environmental Information System. Data used in the paper were obtained from Watershed Allied Telemetry Experimental Research (WATER), which is jointly supported by the Chinese Academy of Sciences Action Plan for West Development Program (grant KZCX2-XB2-09) and the Chinese State Key Basic Research Project (grant 2007CB714400). We would like to sincerely thank the scientists and students in WATER who carried out the field measurements. Mr. Youhua Ran kindly prepared Fig. 1 in the paper. We thank the editor and the two anonymous reviewers for their very suggestive comments. References Avissar, R., Which type of soil vegetation atmosphere transfer scheme is needed for general circulation models: a proposal for a higher-order scheme. Journal of Hydrology 212/213, Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998a. A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. Journal of Hydrology 213, Bastiaanssen, W., Pelgrum, H., Wang, J., Ma, Y., Moreno, J.F., Roerink, G.J., van der Wal, T., 1998b. A remote sensing surface energy balance algorithm for land (SEBAL) 2. Validation. Journal of Hydrology 213, Crago, R.D., Comparison of the evaporative fraction and the Priestley Taylor for parameterizing daytime evaporation. Water Resources Research 32 (5), Franks, S.W., Beven, K.J., Estimation of evapotranspiration at the landscape scale: a fuzzy disaggregation approach. Water Resources Research 33 (12), Guo, X.Y., Remote sensing estimation of evapotranspiration in the Heihe River Basin. PhD Thesis. Cold and Arid Regions Environment and Engineering Research Institute, Lanzhou.

9 X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 17 (2012) He, Y.B., Su, Z.B., Jia, L., Wang, S.L., Regional evapotranspiration of different land covers based on remote sensing. Chinese Journal of Applied Ecology 18 (2), Iqbal, Muhammad, An Introduction to Solar Radiation. Academic Press, Canada, pp Iziomon, M.G., Mayer, H., Matzarakis, A., Downward atmospheric longwave irradiance under clear and cloudy skies: measurement and parameterization. Journal of Atmospheric and Solar-Terrestrial Physics 65 (10), Li, X., Li, X.W., Li, Z.Y., Ma, M.G., Wang, J., Xiao, Q., Liu, Q., Che, T., Chen, E.X., Yan, G.J., Hu, Z.Y., Zhang, L.X., Chu, R.Z., Su, P.X., Liu, Q.H., Liu, S.M., Wang, J.D., Niu, Z., Chen, Y., Jin, R., Wang, W.Z., Ran, Y.H., Xin, X.Z., Ren, H.Z., 2009b. Watershed allied telemetry experimental research. Journal of Geophysical Research-atmospheres 114, D22103, doi: /2008jd Li, X., Li, X.W., Roth, K., Menenti, M., Wagner, W., Preface observing and modeling the catchment scale water cycle. Hydrology and Earth System Sciences 15, , doi: /hess Li, Z.L., Tang, R.L., Wan, Z.M., Bi, Y.M., Zhou, C.H., Tang, B.H., Yan, G.G., Zhang, X.Y., 2009a. A Review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors 10 (9), Liu, S.H., Yu, F., Liu, H.P., Zhang, C.Y., Liang, F.M., Wang, J.H., Study of evapotranspiration mechanism and coupling modeling in arid and semi-arid regions. Acta Scientiarum Naturalium Universitatis Pekinensis 43 (3), Ma, Y.M., Dai, Y.X., Ma, W.Q., Li, M.S., Wang, J.M., Wen, J., Sun, F.L., Satellite remote sensing parameterization of regional land surface heat fluxes over heterogeneous surface of arid and semi-arid areas. Plateau Meteorology 23 (2), Ma, Y.M., Wang, J.M., Massimo Menenti, Wim Bastiaanssen, The distribution and seasonal variation of regional net radiation in HEIFE area. Scientia Atmospherica Sinica 21 (6), Ma, Y.M., Menenti, M., Feddes, R.A., Parameterization of heat fluxes at heterogeneous surface by integrating satellite measurements with surface layer and atmospheric boundary layer observations. Advances in Atmospheric Sciences 27 (2), Pang, W.H., Xu, H.Q., Li, W., Cai, W.H., Zhang, C.G., Regional evapotranspiration estimation in Quanzhou city by using remote sensing technology. Chinese Journal of Agrometeorology 28 (2), Roerink, G.J., Su, Z., Menenti, M., S-SEBI: a simple remote sensing algorithm to estimate the surface energy balance. Physics and Chemistry of the Earth 25 (2), Shang, X., Liu, S.M., Xu, Z.W., Wang, W.Z., Investigation of spatial representativeness for surface flux measurements in the Heihe River Basin. Advances In Earth Science 24 (7), Sobrino, J.A., Raissouni, N., Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing 21 (2), Song, J., Gao, W., An improved method to derive surface albedo from narrowband AVHRR Satellite data: narrowband to broadband conversion. Journal of Applied Meteorology and Climatology 38 (2), Song, Y., Study on method of estimated evapotranspiration of farmland by remote sensing in arid region. PhD Thesis. Cold and Arid Regions Environment and Engineering Research Institute, Lanzhou. Su, Z., The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences 6 (1), Su, Z., Menenti, M., Mesoscale climate hydrology: the contribution of the new observing systems Pre-execution phase. Final Report, USP-2, 99-05, Beleidscommissie Remote Sensing, DLOW in and Staring Centre. Su, Z., Pelgrum, H., Menenti, M., Aggregation effects of surface heterogeneity in land surface processes. Hydrology and Earth System Sciences 3 (4), Ulivieri, C., Castronuovo, M.M., Francioni, R., Cardillo, A., A split window algorithm for estimating land surface temperature from satellites. Advances in Space Research 14 (3), Verstraeten, W.W., Veroustraete, F., Feyen, J., Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: towards an operational processing chain for integrated optical and thermal sensor data products. Remote Sensing of Environment 96 (2), Wu, G.X., Liu, H., Zhang, B.L., Wang, H.J., Zhang, R.H., Li, X., (J.P. Peixoto, A.H. Oort, Trans.) Physics Climate. Meteorological Press, Beijing, pp Wu, J.K., Ding, Y.J., Wei, Z., Wang, G.X., Characteristics of radiation budget in intercropping field in the middle reach of Heihe River Basin. Plateau Meteorology 26 (2), Xin, X., Liu, Q., The two-layer surface energy balance parameterization scheme (TSEBPS) for estimation of land surface heat fluxes. Hydrology and Earth System Sciences 14 (3), Zhang, R.H., Sun, X.M., Zhu, Z.L., Su, H.B., Tang, X.Z., A remote sensing model for monitoring soil evaporation based on differential thermal inertia and its validation. Science in China (Series D) 46 (4), Zhang, W.C., Liu, S.C., Jiang, J.J., Zhao, D.Z., GIS-based retrieval of surface flux and evapotranspiration over Heihe River Basin from remote sensing data. Advances in Marine Science (22Suppl.),