Verification of land surface evapotranspiration estimation from remote sensing spatial contextual information

Estimation of evapotranspiration (ET) is of great significance in modeling the water and energy interactions between land and atmosphere. Negative correlation of surface temperature (T_s) versus vegetation index (VI) from remote sensing data provides diagnosis on the spatial pattern of surface soil moisture and ET. This study further examined the applicability of T_s–VI triangle method with a newly developed edges determination technique in estimating regional evaporative fraction (EF) and ET at MODIS pixel scale through comparison with large aperture scintillometer (LAS) and high‐level eddy covariance measurements collected at Changwu agro‐ecological experiment station from late June to late October, 2009. An algorithm with merely land and atmosphere products from MODIS onboard Terra satellite was used to estimate the surface net radiation (R_n) and soil heat flux. In most cases, the estimated instantaneous R_n was in good agreement with surface measurement with slight overestimation by 12 W/m². Validation results from LAS measurement showed that the root mean square error is 0.097 for instantaneous EF, 48 W/m² for instantaneous sensible heat flux, and 30 W/m² for daily latent heat flux. This paper successfully presents a miniature of the overall capability of T_s–VI triangle in estimating regional EF and ET from limited number of data. For a thorough interpretation, further comprehensive investigation needs to be done with more integration of remote sensing data and in‐situ surface measurements. Copyright © 2011 John Wiley & Sons, Ltd.     

A change detection algorithm for terrain surface moisture mapping based on multi‐year passive microwave remote sensing (Examples of SSM/I and TMI Channels)

As an indication of the surface polarized emission, a polarization index (PI) of microwave radiance from the terrain surface (half‐space of canopy‐soil land) is derived from the radiative transfer model. This PI separates the radiance effects of the canopy‐soil moisture and interference from surface roughness and atmosphere, and is suitable to describe the change of terrain surface moisture, especially for extreme drought or flood conditions. As an example, the statistics of the monthly average < PI > from 6 years' data of the Defense Meteorological Satellite Program (DMSP) SSM/I observations at the lowest frequency 19·35 GHz channel as available are applied for the demonstration of the surface moisture status over a large and heterogeneous territory such as China. The deviation of the PI data at the same month from the average < PI > , i.e. Δ_nPI(≡(PI? < PI>)/ < PI>), gives prominence to focusing moisture variation of terrain surface, and its anomaly shows possible drought or flood occurrence in extreme conditions. The Δ_nPI mapping is validated by the typical examples of the drought in China's Shanxi area in May 2001 and the flood around China's Yangtze River in August 1998, respectively. Our approach is recommended for lower frequency channels to minimize the influence from vegetation canopy for future applications (such as the channels of the Advanced Microwave Scanning Radiometer [AMSR‐E] launched in May 2002 and microwave imaging radiometer of China's Fengyun satellite series). When the monthly < PI > and the ground truth of average volumetric moisture < m_v > of the region are correctly evaluated, it is tractable to retrieve the soil land surface moisture by using the PI data at the same month and the same region without much knowledge of surface roughness, vegetation canopy and other factors. As an example, the retrieval of m_v is favourably tested by using the Tropical Rainfall Measuring Mission (TRMM) Tropical Microwave Imager (TMI) data. Copyright © 2007 John Wiley & Sons, Ltd.     

Estimation of annual actual evapotranspiration from nonsaturated land surfaces with conventional meteorological data

Land surface evapotranspiration is an important component both in earth surface heat and water bal-ance, on whose budgets weather and climate depend, to a great extent, for their changes are responsible for the formation and variation of vegetation features on the globe. Besides, the evapotranspiration is an im-portant topic of short-term flood forecasting and the estimation of runoff from mountainous sides. As a result, the problem as to the evapotranspiration has been one of the concerns in …     

Modeling the land surface reflectance for optical remote sensing data in rugged terrain

A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively,the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model.A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper.The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.     

Evaluating the SEBS‐estimated evaporative fraction from MODIS data for a complex underlying surface

With the complex nature of land surfaces, more attention should be paid to the performance of remotely sensed models to estimate evapotranspiration from moderate and low spatial resolution data. Taking into account the characteristic of a stable evaporative fraction (EF) in the daytime, this paper uses the surface energy balance system (SEBS) to estimate the EF from MODIS data for a subtropical evergreen coniferous plantation in southern China and evaluates the stability of the SEBS model in estimating the EF under complex surface conditions. The results show that the SEBS‐estimated EF is larger than the measured EF partly because of the serious lack of energy‐balance closure. This difference can be largely reduced by the residual energy correction method. More evaporative land cover within the MODIS pixel is a main reason for the overestimated EF. SEBS underestimates sensible heat flux, and the underestimation of surface available energy also contributes to the overestimation of the EF. The EF estimated from MODIS/Terra data is in agreement with that from MODIS/Aqua data with a coefficient of determination (R²) of 0.552, a mean bias error (BIAS) of 0.028, and a root mean square error (RMSE) of 0.079, which is consistent with the result from in situ measurements. In addition, the estimation of surface available energy from remotely sensed data is evaluated on this complex underlying surface. Compared with in situ measurements, the available energy is underestimated by 28 W m^?2 with an RMSE = 50 W m^?2 and an R² = 0.87. Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of spatial variations of land surface parameters on regional evaporation: a case study with remote sensing data

Most precipitation in watersheds is consumed by evaporation, thus techniques to appraise regional evaporation are important to assess the availability of water resources. Many algorithms to estimate evaporation from remotely sensed spectral data have been developed in the recent past. In addition to differences in the physical parameterization of surface fluxes, these algorithms have different solutions for describing spatial variations of the parameters in the soil–vegetation–atmosphere–transfer (SVAT) continuum. In this study, the necessity to spatially distinguish SVAT parameters for computing surface heat fluxes is analysed for the Naivasha watershed in the Kenyan Rift Valley. Landsat Thematic Mapper (TM) spectral data have been used to first delineate the watershed into 15 hydrological units using surface temperature, normalized difference vegetation index and surface albedo as attributes. Thereafter, semi‐empirical relationships between these TM‐based parameters and other SVAT parameters have been applied to compute the spatial variation of SVAT parameters and the associated evaporation from the different hydrological units. The impact of using watershed‐constant or watershed‐distributed SVAT parameters on the fluxes is analysed. The determination of watershed averaged evaporation with area‐aggregated SVAT parameters is feasible without significant loss of accuracy. Distributed evaporation in heterogeneous watersheds, however, can be investigated only with remote sensing flux algorithms that can account for spatially variable air temperature, surface roughness, surface albedo and the stability correction of the temperature profile due to buoyancy. Erroneous results can be expected if area‐aggregated SVAT parameters are used to calculate local evaporation. As most of the recently developed remote sensing flux algorithms are based on areal constant SVAT parameters, direct applications in watersheds are still limited. Copyright © 2001 John Wiley & Sons, Ltd.     

Regional hydromorphological characterization with continuous and automated remote sensing analysis based on VHR imagery and low‐resolution LiDAR data

Recent developments in remote sensing (RS) technologies lead the way in characterizing river morphology at regional scales and inferring potential channel responses to human pressures. In this paper, a unique regional database of continuous hydromorphological variables (HyMo DB) based on areal and topographic data has been generated from RS analysis. Key riverscape units with specific geomorphic meaning have been automatically mapped for 1700 km² of river floodplains from simultaneous very‐high‐resolution (VHR) near‐infrared aerial imagery and low‐resolution LiDAR‐derived products. A multi‐level, geographical object‐based architecture (GEOBIA) was employed to integrate both spectral and topographic information and generate a regional classifier able to automatically map heterogeneous fluvial patterns in different geographical and topographical contexts of the Piedmont Region (Italy). This HyMo‐generated DB offers a unique set of tools for hydromorphologists and can be exploited for different purposes. For the first time, topographic information can be exploited regionally per riverscape unit class, allowing for quantitative analysis of their regional spatial and statistical variability. In this manner, river types can be automatically characterized and classified using objective and repeatable hydromorphological variables. We discuss the potential of quantifying functional links between riverscape units and their driving processes, a valuable source of information to start assessing and highlighting the entity of potential channel adjustments at the regional scale to human pressures. The HyMo DB can also be integrated with historical, field‐based information to better comprehend current fluvial changes at a local scale. In view of future RS acquisitions, the present approach will result in a suitable procedure for quantitative, objective and continuous monitoring of river evolutions over large scales. This type of hydromorphological characterization will allow regional trends and patterns to be highlighted through time and river management strategies to thus be implemented at both regional and local scales. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluation of remote sensing‐based evapotranspiration estimates using a water transfer numerical simulation under different vegetation conditions in an arid area

Daily actual evapotranspiration (AET) and seasonal AET values are of great practical importance in the management of regional water resources and hydrological modelling. Remotely sensed AET models and Landsat satellite images have been used widely in producing AET estimates at the field scale. However, the lack of validation at a high spatial frequency under different soil water conditions and vegetation coverages limits their operational applications. To assess the accuracies of remote sensing‐based AET in an oasis‐desert region, a total of 59 local‐scale daily AET time series, simulated using HYDRUS‐1D calibrated with soil moisture profiles, were used as ground truth values. Of 59 sampling sites, 31 sites were located in the oasis subarea and 28 sites were located in the desert subarea. Additionally, the locally validated mapping evapotranspiration at high resolution with internalized calibration surface energy balance model was employed to estimate instantaneous AET values in the area containing all 59 of the sampling sites using seven Landsat subimages acquired from June 5 to August 24 in 2011. Daily AET was obtained using extrapolation and interpolation methods with the instantaneous AET maps. Compared against HYDRUS‐1D, the remote sensing‐based method produced reasonably similar daily AET values for the oasis sites, while no correlation was observed for daily AET estimated using these two methods for the desert sites. Nevertheless, a reasonable monthly AET could be estimated. The correlation analysis between HYDRUS‐1D‐simulated and remote sensing‐estimated monthly AET values showed relative root‐mean‐square error values of 15.1%, 12.1%, and 12.3% for June, July, and August, respectively. The root mean square error of the summer AET was 10.0%. Overall, remotely sensed models can provide reasonable monthly and seasonal AET estimates based on periodic snapshots from Landsat images in this arid oasis‐desert region.     

Estimation of near surface soil moisture in a sloping terrain of a Himalayan watershed using ENVISAT ASAR multi‐incidence angle alternate polarisation data

Soil moisture is one of the important input variables in hydrological and water erosion models. The extraction of information on near surface soil moisture from synthetic aperture radar (SAR) is well established mostly for flat terrain and using low incidence angle single polarisation data. The ENVISAT advanced SAR (ASAR) data available in multiple incidence angles and alternate polarisation modes were investigated in this study for soil moisture estimation in sloping terrain. The test site was Sitla Rao watershed in the Lesser Himalayas of northern India. Empirical models were developed to estimate near surface soil moisture in bare agricultural fields using alternate polarisation ASAR data. Both soil moisture and surface roughness field measurements were performed during the satellite passes. Backscatter from medium incidence angle (IS‐4) and vertical‐vertical (VV) polarisation signal is correlated better with volumetric soil moisture content compared to other incidence angles. The model parameters were further improved, and soil moisture estimation was refined by combining medium incidence angle (IS4) vertical‐horizontal polarisation response as another variable along with VV polarisation response. The effect of slope on the radar backscatter was minimized by incorporating local incidence angles derived from an ASTER DEM. Copyright © 2012 John Wiley & Sons, Ltd.     

卫星遥感数据评估黄土高原陆面干湿程度研究

卫星遥感数据具有估算时空尺度上地表参量的优势,在陆地环境状况评估和监测等方面有很大的应用潜力.本文利用美国地球观测系统卫星搭载中等分辨率成像光谱仪（EOS/MODIS）在黄土高原2002-2010年期间获取的每16天归一化植被指数（NDVI）和每日地表温度（LST）数据,分析了黄土高原地区LST-NDVI空间的基本特征.结果发现：当研究区域足够大且遥感数据时间序列足够长时,LST-NDVI空间中（NDVI,LST）散点并非呈三角形或梯形分布.为了能够利用EOS/MODIS的NDVI和LST数据正确地评估陆面的干湿状况,本文给出了利用数据集合法确定LST-NDVI空间中干边和湿边的数值,即在LST-NDVI空间中,利用NDVI等值区间内LST最大值和最小值的集合代表干边和湿边的数值,并进一步证明了在LST-NDVI空间中干边和湿边数值并非呈线性关系.在分析LST-NDVI空间特征的基础上,通过构建地表温度-植被干旱指数（TVDI）,探讨其在评估黄土高原地区陆面的干湿状况的应用潜力.结果表明：由TVDI距平表征的陆面的干湿程度与局地降水距平有很好的关联性,二者在时空分布上有较好的对应关系.在我国陇东黄土高原塬区,TDVI数值与地面观测的表层土壤湿度有很好的相关性,相关系数在0.67以上,并通过显著性为1%的检验.由此说明：如果合理选取干边和湿边的数值,TDVI可应用于区域陆面干湿程度的客观评估.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?

Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd.     

Remote sensing and ground‐based measurements of evapotranspiration in an extreme cold Patagonian desert

Accurate estimates of seasonal evapotranspiration (ET) at different temporal and spatial scales are essential for understanding the biological and environmental determinants of ecosystem water balance in arid regions and the patterns of water utilization by the vegetation. For this purpose, remote sensing ET estimates of a Patagonian desert in Southern Argentina were verified with field measurements of soil evaporation and plant transpiration using an open top chamber. Root distribution and seasonal variation in soil volumetric water content were also analysed. There was a high correlation between remote sensing and field measurements of ecosystem water fluxes. A substantial amount of the annual ET occurred in spring and early summer (73.4 mm) using winter rain stored in the soil profile and resulting in water content depletion of the upper soil layers. A smaller amount of annual ET was derived from few rainfall events occurring during the mid or late summer (41.4 mm). According to remote sensing, the 92.9% of the mean annual precipitation returns to the atmosphere by transpiration or evaporation from the bare soil and by canopy interception. Only 7.1% infiltrates to soil layers deeper than 200 cm contributing to the water table recharge. Fourier time series analysis, cross‐correlation methods and multiple linear regression models were used to analyse 11 years of remote sensing data to assess determinants of water fluxes. A linear model predicts well the variables that drive complex ecosystem processes such as ET. Leaf area index and air temperature were not linearly correlated to ET because of the multiple interaction among variables resulting in time lags with ET variations and thus these two variables were not included in the linear model. Soil water content, the fraction of photosynthetic active radiation and precipitation explained 86% of the ET monthly variations. The high volumetric water content and the small seasonal variations at 200‐cm depth were probably the result of little water uptake from deeper soil horizons by roots with low hydraulic conductivity. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluation of evapotranspiration models over semi‐arid and semi‐humid areas of China

Evapotranspiration (ET) is a critical component in the hydrological cycle. However, its actual values appear to be difficult to obtain, especially in areas in which precipitation has high inter‐annual variability. Here, we evaluated eight commonly used ET models in semi‐arid and semi‐humid areas of China. The order of overall performance from best to worst is as follows: the revised Priestley–Taylor model (PT‐JPL, 0.71, 1.65 [18.37%], 4.72 [49.19%]) a a Statistics (model abbreviation, coefficient of determination, bias [relative value], standard deviation [relative value]).
, the modified PT‐JPL model (M1‐PT‐JPL, 0.67, ?0.68 [7.56%], 3.87 [40.31%]), the Community Land Model (CLM, 0.68, ?2.52 [28.01%], 5.10 [53.17%]), the modified PT‐JPL model (M2‐PT‐JPL, 0.63, 0.57 [6.27%], 5.04 [52.52%]), the revised Penman–Monteith model (RS‐PM, 0.62, 3.56 [37.40%], 6.11 [63.68%]), an empirical model (Wang, 0.59, ?1.04 [11.57%], 5.61 [58.43%]), the advection‐aridity model (AA, 0.55, 5.56 [61.78%], 7.45 [77.60%]), and the energy balance model (SEBS, 0.35, 5.11 [56.72%], 9.43 [98.18%]). The performance of all of the models is comparably poor in winter and summer, except for the PT‐JPL model, and relatively good in spring and autumn. Because of the vegetation control on ET, the Wang, RS‐PM, PT‐JPL, M1‐PT‐JPL, and M2‐PT‐JPL models perform better for cropland, whereas the AA model, SEBS model and CLM perform better for grassland. The CLM, PT‐JPL, and Wang models perform better in semi‐arid region than in semi‐humid region, whereas the opposite is true for SEBS and RS‐PM. The AA, M1‐PT‐JPL, and M2‐PT‐JPL models perform similarly in semi‐arid and semi‐humid regions. When considering the inter‐annual variability in precipitation, the Wang model has relatively good performance under only some annual precipitation conditions; the performance of the PT‐JPL and AA models is reduced under conditions of high precipitation; the two modified PT‐JPL models inherited the steady performance of the PT‐JPL model and improved the performance under conditions of high annual precipitation by the modification of the soil moisture constraint. RS‐PM is more appropriate for humid conditions. CLM and PT‐JPL models could be effectively applied to all precipitation conditions because of their good performance across a wide annual precipitation range. Copyright © 2016 John Wiley & Sons, Ltd.     

Spatially distributed hydrotope‐based modelling of evapotranspiration and runoff in mountainous basins

River basins in mountainous regions are characterized by strong variations in topography, vegetation, soils, climatic conditions and snow cover conditions, and all are strongly related to altitude. The high spatial variation needs to be considered when modelling hydrological processes in such catchments. A complex hydrological model, with a great potential to account for spatial variability, was developed and applied for the hourly simulation of evapotranspiration, soil moisture, water balance and the runoff components for the period 1993 and 1994 in 12 subcatchments of the alpine/pre‐alpine basin of the River Thur (area 1703 km²). The basin is located in the north‐east of the Swiss part of the Rhine Basin and has an elevation range from 350 to 2500 m a.s.l. A considerable part of the Thur Basin is high mountain area, some of it above the tree‐line and a great part of the basin is snow covered during the winter season. In the distributed hydrological model, the 12 sub‐basins of the Thur catchment were spatially subdivided into sub‐areas (hydrologically similar response units—HRUs or hydrotopes) using a GIS. Within the HRUs a hydrologically similar behaviour was assumed. Spatial interpolations of the meteorological input variables wereemployed for each altitudinal zone. The structure of the model components for snow accumulation and melt, interception, soil water storage and uptake by evapotranspiration, runoff generation and flow routing are briefly outlined. The results of the simulated potential evapotranspiration reflect the dominant role of altitudinal change in radiation and albedo of exposure, followed by the influence of slope. The actual evapotranspiration shows, in comparison with the potential evapotranspiration, a greater variability in the lower and medium altitudinal zones and a smaller variability in the upper elevation zones, which was associated with limitations of available moisture in soil and surface depression storages as well as with the evaporative demand of the local vegetation. The higher altitudinal dependency and variability of runoff results from the strong increase in precipitation and the decrease in evaporation with increased altitude. An increasing influence of snow cover on runoff as well as evapotranspiration with altitude is obvious. The computed actual evapotranspiration and runoff were evaluated against the observed values of a weighting lysimeter and against runoff hydrographs. Copyright © 1999 John Wiley & Sons, Ltd.     

Regional‐scale sedimentation process models from airborne gamma ray remote sensing and digital elevation data

Airborne gamma ray survey data were used to provide information on potassium, thorium and uranium concentrations in surface soil and rock in arid central Australia. Spatial patterns in these radioelements allow tracing of paths of sediment at catchment scale. Survey elevation data are combined with contour data to produce digital elevation models for terrain analysis, tracing of sediment flow paths and modelling of extreme floods. Gamma ray data show consistent variation with slope, a limited range of drainage areas, and erosion/deposition models derived from the conservation of mass equation. Supply‐limited sediment transport models give a reasonable reproduction of observed radioelement distribution but some elements of the distribution pattern reflect the area inundated by 500–1000 year floods rather than the effects of simple downslope movement. Partial area sediment supply models are derived by downstream accumulation of erosion and deposition rates calculated using the conservation of mass equation with transport laws based on slope alone and stream power. Comparison with observed radioelement patterns suggests that both transport laws apply in different parts of the landscape. Regional‐scale sediment transport models will require a range of models depending on location in the landscape and event frequency. This approach may allow estimation of sediment delivery ratios. Copyright © 2001 John Wiley & Sons, Ltd.     

应用EOS/MODIS卫星资料反演与估算西北雨养农业区地表能量和蒸散量

应用定西地区的陆面过程野外观测资料和四次EOS_MODIS卫星资料,对典型的西北半干旱雨养农业区的基本地表特征参数进行反演,在此基础上对各能量通量和蒸散量进行估算,进而分别分析其季节和空间变化.结果表明：应用卫星数据估算的各参量的相对误差基本都在20%以内,其空间和时间变化基本反映了当地的实际情况;区域各通量和日蒸散量分布极不均匀,表现为四季相差比较大的单峰型变化特征,冬季最小、其次是秋季、夏春两季最大;与面积较大的中、低植被覆盖度区域相比,小范围的高植被覆盖度区域的地表净辐射、潜热和日蒸散量相对较高,土壤热通量和显热通量相对较低,并且都在春、夏季节表现得更加显著.低、中植被覆盖度区的各能量通量的季节变化不显著的特征反映了半干旱西北雨养农业区土壤的干土层相对比较厚的特征.