ET mapping for agricultural water management: present status and challenges

Evapotranspiration (ET) is an essential component of the water balance. Remote sensing based agrometeorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. In this paper, a literature review was done to evaluate numerous commonly used remote sensing based algorithms for their ability to estimate regional ET accurately. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. However, there are opportunities to further improving these models for accurately estimating all energy balance components. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (∼10 to 200 ha). This will be constrained further if the thermal sensors on future Landsat satellites are abandoned. However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from Landsat/ASTER/MODIS images using same/other-sensor high resolution multi-spectral images.     

Estimating hourly crop ET using a two-source energy balance model and multispectral airborne imagery

Efficient water use through improved irrigation scheduling is expected to moderate fast declining groundwater levels and improve sustainability of the Ogallala Aquifer. An accurate estimation of spatial actual evapotranspiration (ET) is needed for this purpose. Therefore, during 2007, the Bushland ET and Agricultural Remote Sensing Experiment (BEAREX07) was conducted at the USDA-ARS, in Bushland, Texas, to evaluate remote sensing (RS)-based surface energy balance models. Very high-resolution aircraft images were acquired using the Utah State University airborne multispectral system. Instantaneous ET was estimated using a two-source energy balance model (TSM). A minor modification was made in the calculation of sensible heat fluxes to improve ET estimation. Furthermore, a sensitivity analysis of selected variables was conducted to evaluate their effect on ET estimation. Data from four weighing lysimeters, planted to sorghum and corn, were used for evaluating ET predictions. Instantaneous ET was predicted with mean bias error and root mean square error of 0.03 and 0.07 mm h⁻¹ (4.3 and 11.7%), respectively. Results indicated that crop height, roughness length for momentum transfer, clumping factor and soil resistance sub-models need to be refined. Nevertheless, the application of the TSM using high-resolution RS imagery in the Southern High Plains is promising.     

Quantifying water stress on wheat using remote sensing in the Yaqui Valley, Sonora, Mexico

Remote sensing can allow a more efficient irrigation water management by applying the water when crops require it or when symptoms of water stress appear. In this study, the spatial and temporal distribution of the water deficit index (WDI) and crop evapotranspiration (ET) in wheat were determined through analysis of satellite-based remote sensing images in the Yaqui Valley, Sonora, México. We utilize an empirical model based on the canopy temperature–vegetation cover relationship methodology known as the Moran's trapezoid. We analyze and discuss the spatial and temporal distributions of WDI and ET at the regional and local scales. Results show a linear relationship (R² = 0.96) between the values of WDI and the number of days elapsed since the last irrigation. The water deficit index could be utilized to estimate the quantity of available water in wheat and to know the degree of stress presented by the crop. Advantages offered by this methodology include obtaining WDI and evapotranspiration values in zones with partial or null vegetation cover and for large irrigation schemes lacking the necessary data for traditional water management.     

Evaluating eddy covariance cotton ET measurements in an advective environment with large weighing lysimeters

Eddy covariance (EC) systems are being used to assess the accuracy of remote sensing methods in mapping surface sensible and latent heat fluxes and evapotranspiration (ET) from local to regional scales, and in crop coefficient development. Therefore, the objective was to evaluate the accuracy of EC systems in measuring sensible heat (H) and latent heat (LE) fluxes. For this purpose, two EC systems were installed near large monolithic weighing lysimeters, on irrigated cotton fields in the Texas High Plains, during the months of June and July 2008. Sensible and latent heat fluxes were underestimated with an average error of about 30%. Most of the errors were from nocturnal measurements. Energy balance (EB) closure was 73.2–78.0% for daytime fluxes. Thus, daylight fluxes were adjusted for lack of EB closure using the Bowen ratio/preservation of energy principle, which improved the resulting EC heat flux agreement with lysimetric values. Further adjustments to EC-based ET included nighttime ET (composite) incorporation, and the use of ‘heat flux source area’ (footprint) functions to compensate ET when the footprint expanded beyond the crop field boundary. As a result, ET values remarkably matched lysimetric ET values, with a ‘mean bias error ± root mean square error’ of −0.03 ± 0.5 mm day⁻¹ (or −0.6 ± 10.2%).     

Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of N.E. Spain

Accurate estimation of actual evapotranspiration (ET_a) is essential for effective local or regional water management. At a local scale, ET estimates can be made accurately considering a soil-plant-atmospheric system, if adequate meteorological-ground data or ET measurements are available. However, at a regional scale, ET_a values cannot be measured directly and the estimates are frequently subject to errors. Although it is possible to extrapolate to the regional scale from local (point) data of meteorological stations, the relative sparse coverage of ground estimate can make this problematic without some spatial analysis to demonstrate the similarity of the climate in the area. The introduction of remote sensing data and techniques offers alternatives both to estimate variables (i.e. radiation) and parameters (i.e. ET) with few spatial restrictions, thus, it provides potential advantages to the regional ET_a computation. In particular, the use of remote sensing procedures like the surface energy balance-based algorithms (SEB) have been successfully applied in different climates, enabling the estimation of ET_a at local and regional scales. A proper variation of the Surface Energy Balance Algorithm for Land (SEBAL) was applied to 4 years of data for the Flumen District in the Ebro Basin at the N.E. of Spain. Results obtained show that the remote sensing algorithm can provide accurate daily ET_a estimations as compared with lysimeter measurements of daily ET values for two crop plots: one with a reference grass and other with maize or wheat as function of the season. Also a comparison between ET_a and the reference and crop ET values applying the Penman-Monteith method was carried out. The comparison analysis consider an accepted error difference of 1.0 mm d⁻¹ (20% of error) for local scale, the ET_a values for the grass show a bias of 0.30 mm d⁻¹ against the ET_grass and a bias of 0.36 mm d⁻¹ against ET_o. Differences between ET_maize or ET_wheat and ET_a against their average showed an acceptable agreement for the field with s_diff ± 0.6 mm d⁻¹. For the crop fields at regional scale external causes associated to atmospheric and surface variations (i.e. land preparation) rather to the remote sensing algorithm made difficult to determine a percentage of error. Finally, ET_a values were obtained at regional scale and it was demonstrated that using the remote sensing improve significantly the crop ET estimations computed in the area using traditional methods.     

Using remote sensing to evaluate the spatial variability of evapotranspiration and crop coefficient in the lower Rio Grande Valley,New Mexico

Pecan is a major crop in the lower Rio Grande Valley (LRGV), New Mexico. Currently, about 11,000 ha of pecan orchards at various stages of growth are consuming about 40% of irrigation water in the area. Pecan evapotranspiration (ET) varies with age, canopy cover, soil type and method of water management. There is a need for better quantification of pecan ET for the purpose of water rights adjudication, watershed management and agronomical practices. This paper describes a process where remote sensing information from Landsat-5 and Landsat-7 were combined with ground level measurements to estimate pecan ET and field scale actual crop coefficient (K _c) for the LRGV. The results showed that annual pecan water use for 279 fields ranged from 498 to 1,259 mm with an average water use of 1,054 mm. For fields with NDVI > 0.6 (normalized difference vegetation index), which represented mature orchards (total of 232 fields), the annual water use ranged from 771 to 1,259 mm with an average water use of 1,077 mm. The results from remote sensing model compared reasonably well with ground level ET values determined by an eddy covariance system in a mature pecan orchard with an average error of 4% and the standard error of estimate (SEE) ranging from 0.91 to 1.06 mm/day. A small fraction (5%) of the pecan fields were within the range of maximum ET and K _c.     

青海省农业生态区参考作物蒸散量的遥感反演

根据青海省农业生态区15个气象站2003年气象资料,应用FAO推荐的Penman-Monteith方程计算参考作物蒸散量(ET0),利用MODIS高程(DEM)、地表温度(LST)及法国SPOT卫星的归一化植被指数(NDVI)遥感影像资料,提取遥感数据并耦合到时间分辨率为旬,空间分辨率为1km,将其与计算所得ET0进行相关分析,运用MATLAB软件进行模型拟合,获得该地区的ET0遥感反演模型及其适用条件,使用Arcgis9.3对利用该模型反演的结果进行了空间分布规律分析。研究结果表明,2003年7—11月各旬遥感因子DEM、NDVI和LST与ET0的线性关系显著,其中DEM与ET0呈显著负相关关系,NDVI和LST与ET0呈极显著正相关关系;获得了该地区基于遥感数据的旬ET0三元线性遥感反演模型,该模型通过α=0.01的F和t显著性检验,模型效果极显著,适用期为7—10月;对2004年计算及反演结果表明,ET0空间分布从西北往东南方向递增,低海拔处往高海拔处递减;模型反演平均相对误差为-1.9%,有较好的反演结果。     

A review of downscaling methods for remote sensing-based irrigation management: part I

High-resolution daily evapotranspiration (ET) maps would greatly improve irrigation management. Numerous ET mapping algorithms have been developed to make use of thermal remote sensing data acquired by satellite sensors. However, adoption of remote sensing-based ET maps for irrigation management has not been feasible due to inadequate spatial and temporal resolution of ET maps. Data from a coarse spatial resolution image in agricultural fields often cause inaccurate ET estimation because of a high level of spatial heterogeneity in land use. Image downscaling methods have been utilized to overcome spatial and temporal scaling issues in numerous remote sensing applications. In the field of hydrology, the image downscaling method has been used to improve spatial resolution of remote sensing-based ET maps for irrigation scheduling purposes and thus improves estimation of crop water requirements. This paper (part I) reviews downscaling methods to improve spatial resolution of land surface characteristics such as land surface temperature or ET. Each downscaling method was assessed and compared with respect to their capability of downscaling spatial resolutions of images. The companion paper (part II) presents review of image fusion methods that are also designed to increase spatial resolutions of images by integrating multi-spectral and panchromatic images.     

基于地面观测和Sentinel-2数据的玉米实际蒸散发估算

基于遥感技术估算作物蒸散发(Evapotranspiration,ET)对农业用水效率评价和精量灌溉决策具有重要意义。结合Sentinel-2数据和农田连续地面观测资料,利用混合双源蒸散发模型(Hybrid dual-source scheme and trapezoid framework-based evapotranspiration model,HTEM)对宁夏回族自治区中卫市2019年两个试验田玉米主要生育期(5—8月)的蒸散发量进行估算,并用水量平衡法对遥感估算结果进行验证和评价。结果表明:Sentinel-2数据具有高时空分辨率,能够与研究区复杂的种植地块相匹配,减少了混合像元的数量;遥感反演参数与地面观测数据拟合度较高,研究区2019年遥感反演的玉米田净辐射量均方根误差为36.256 W/m²。利用HTEM模型估算可得,主要生育期内研究区两个玉米试验田的日均实际蒸散发量分别为4.269 mm/d和4.339 mm/d,实际蒸散发总量分别为525.114 mm和533.690 mm,其中植被蒸腾量分别为363.483 mm和358.196 mm,生育初期主要以土壤蒸发形式消耗水分,随着作物的生长,在生育中后期主要以植被蒸腾的形式消耗水分。ET遥感反演结果与水量平衡结果之间差别不显著,两个观测点绝对误差分别为13.533 mm和7.774 mm。因此,结合地面连续观测系统和Sentinel-2数据估算研究区玉米生育阶段蒸散发量具有较高的精度,可为作物耗水规律研究及区域农业水管理提供技术支撑。     

Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation

Evapotranspiration (ET) is one of the indicators of water use efficiency. Periodic information of ET based on remote sensing is useful for an on-demand irrigation (ODI) management. The main objective of this paper was to develop an ET data assimilation scheme to optimize the parameters of an agro-hydrology model for ODI scheduling. The soil, water, atmosphere, and plant (SWAP) simulation model has been utilized for this purpose. We computed remote sensing-based ET for a wheat field in the Sirsa Irrigation Circle, Haryana, in India using 18 cloud-free moderate resolution imaging spectroradiometer images taken between December 2001 and April 2002. The surface energy balance algorithm for land (SEBAL) was used for this purpose. Because ET estimates from SEBAL provide information on the surface soil moisture state, they were treated as observations to estimate unknown parameters of the SWAP model via a stochastic data assimilation (genetic algorithm) approach. The SWAP parameters were optimized by minimizing the residuals between SEBAL and SWAP model-based ET values. The optimized parameters were used as input to SWAP to estimate soil water balance for ODI scheduling. The results showed that the selected parameters (i.e. sowing, harvesting, and irrigation scheduling dates) were successfully estimated with the data assimilation methodology. The SWAP model produced reasonable states of water balance by assimilating ET observations. The root mean square of error was 0.755 and 2.132 cm³/cm³ for 0–15 and 15–30 cm soil depths the same layers, respectively. With optimized parameters for ODI, SWAP predicted higher yield and water use efficiency than traditional farmer’s irrigation criteria. The data assimilation methodology produced can be considered as an operational tool at the field scale to schedule irrigation or predict irrigation requirements from remote sensing-based ET.     

Estimation of water consumption and crop water productivity of winter wheat in North China Plain using remote sensing technology

The North China Plain (NCP) is one of the most water stressed areas in the world. The water consumption of winter wheat accounts for more than 50% of the total water consumption in this region. An accurate estimate of the evapotranspiration (ET) and crop water productivity (CWP) at regional scale is therefore key to the practice of water-saving agriculture in NCP. In this research, the ET and CWP of winter wheat in 83 counties during October 2003 to June 2004 in NCP were estimated using the remote sensing data. The daily ET was calculated using SEBAL model with NOAA remote sensing data in 17 non-cloud days whereas the reference daily crop ET was estimated using meteorological data based on Hargreaves approach. The daily ET and the total ET over the entire growing season of winter wheat were obtained using crop coefficient interpolation approach. The calculated average and maximum water consumption of winter wheat in these 83 counties were 424 and 475 mm, respectively. The calculated daily ET from SEBAL model showed good match with the observed data collected in a Lysimeter. The error of ET estimation over the entire growing stage of winter wheat was approximately 4.3%. The highest CWP across this region was 1.67 kg m⁻³, and the lowest was less than 0.5 kg m⁻³. We observed a close linear relationship between CWP and yield. We also observed that the continuing increase of ET leads to a peaking and subsequent decline of CWP, which suggests that the higher water consumption does not necessarily lead to a higher yield.     

一种简化蒸散发遥感反演模型及其在灌区的应用

提出简化的地表能量平衡模型(SSEB)将站点作物蒸散发量递推到整个灌区。首先由气象观测资料计算作物潜在蒸散发量,结合Landsat ETM+60 m热红外波段反演的地表温度差异把站点单日ET扩展到灌区,再融合ETM+和MODIS 1 km热红外时间系列数据,进一步提取全年灌区尺度蒸散发分布图。用湖北漳河灌区2000年的影像资料,将SSEB与SEBAL模型计算结果进行对比,结果表明,二者的决定系数R2达到了0.89。漳河灌区蒸散发主要来源于二、三干渠,年总蒸散发量中,作物蒸发蒸腾占67%,林地蒸散发占17%,水面加上裸地等其它用地的无效蒸散发占16%。     

Remote sensing to estimate ET-fluxes and the performance of an irrigation district in southern Italy

Satellite remote sensed data on canopy biophysical properties, ground data and agro-meteorological information were combined to estimate evapotranspiration (ET) fluxes of orange orchards using a modified Penman–Monteith equation. The study was carried out during the irrigation season 2004 in an irrigation district, cover for about 95% with orange groves, of 1550 ha located in eastern Sicily (Italy). The spatial pattern in ET-fluxes have been analysed using IKONOS high-resolution satellite and hyper-spectral ground data acquired and processed for the study-area. The remote estimates of ET-fluxes varied between 1.3 and 5.7 mm/day, with a daily average value of about 4.2 mm, showing a good agreement with crop ET values determined as residual of soil water balance of selected ground control sites. Crop coefficient estimates ranged between 0.22 and 1.08 showing positive correlations with percentages of ground cover (C_g) increasing from 30 to 80% ground shading and with LAI values. By comparing ET estimates with water volumes supplied in each sub-district of the study-area, the performance indicator “IP” was evaluated, allowing to rank the conditions of un-fulfilment of crop water requirements by public and private water distribution systems. Generally, out of 29 sub-districts, 14 had “IP” values less than 50%, revealing a sub-optimal water supply for the study-area.     

多源遥感降水评估及其在水文模拟中的应用

多源遥感降水产品为无资料或缺资料地区的水文过程模拟提供了新的高质量的数据来源。以美国爱荷华州Iowa-Cedar中型流域为例,基于地面雨量站降水数据CPC-U定量评估了5种多源遥感降水产品(NLDAS2、Stage IV、TMPARP、TMPART和IMERG)的精度,并结合分布式水文模型DRIVE(Dominant river tracing-Routing Integrated with Variable Infiltration Capacity Environment)进行了水文效用评估。研究表明:①NLDAS2和Stage IV都能很好地捕捉降水事件,与CPC-U具有良好的一致性,其次是IMERG和TMPARP,日尺度上相关系数达到0.63~0.80,偏差为-0.21%~15.37%,探测率为0.61~0.74,而TMPART产品精度较低;②采用不同降水数据驱动分布式水文模型进行径流模拟,NLDAS2和CPC-U表现效果最好,纳什效率系数分别为0.82和0.8,其次是Stage IV和TMPARP,纳什效率系数分别为0.79和0.63,而IMERG和3B42RT表现较差,纳什效率系数分别为0.46和-1.09。对比结果表明,NLDAS2、Stage IV降水产品精度评估表现较好,适合在Iowa-Cedar流域进行水文模拟应用,其次是TMPARP和IMERG,但IMERG水文效用评估相对较差,说明多源遥感降水数据融合产品更适合于水文模拟应用。     

Combining an ecological model with remote sensing and GIS techniques to monitor soil water content of croplands with a monsoon climate

Soil water is an important factor affecting photosynthesis, transpiration, growth, and yield of crops. Accurate information on soil water content (SWC) is crucial for practical agricultural water management at various scales. In this study, remotely sensed parameters (leaf area index, land cover type, and albedo) and spatial data manipulated using the geographic information system (GIS) technique were assimilated into the boreal ecosystem productivity simulator (BEPS) model to monitor SWC dynamics of croplands in Jiangsu Province, China. The monsoon climate here is characterized by large interannual and seasonal variability of rainfall causing periods of high and low SWC. Model validation was conducted by comparing simulated SWC with measurements by a gravimetric method in the years 2005 and 2006 at nine agro-meteorological stations. The model-to-measurement R² values ranged from 0.40 to 0.82. Nash-Sutcliffe efficiency values were in the range from 0.10 to 0.80. Root mean square error (RMSE) values ranged from 0.028 to 0.056 m³ m⁻³. Simulated evapotranspiration (ET) was consistent with ET estimated from pan evaporation measurements. The BEPS model successfully tracked the dynamics and extent of the serious soil water deficit that occurred during September-November 2006. These results demonstrate the applicability of combining process-based models with remote sensing and GIS techniques in monitoring SWC of croplands and improving agricultural water management at regional scales in a monsoon climate.     

Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration,crops coefficients,leaf area index and remotely-sensed vegetation indices

The monitoring of crop production and irrigation at a regional scale can be based on the use of ecosystem process models and remote sensing data. The former simulate the time courses of the main biophysical variables which affect crop photosynthesis and water consumption at a fine time step (hourly or daily); the latter allows to provide the spatial distribution of these variables over a region of interest at a time span from 10 days to a month. In this context, this study investigates the feasibility of using the normalised difference vegetation index (NDVI) derived from remote sensing data to provide indirect estimates of: (1) the leaf area index (LAI), which is a key-variable of many crop process models; and (2) crop coefficients, which represent the ratio of actual (AET) to reference (ET₀) evapotranspiration.A first analysis is performed based on a dataset collected at field in an irrigated area of the Haouz plain (region of Marrakesh, Central Morocco) during the 2002–2003 agricultural season. The seasonal courses of NDVI, LAI, AET and ET₀ have been compared, then crop coefficients have been calculated using a method that allows roughly to separate soil evaporation from plant transpiration. This allows to compute the crop basal coefficient (K_cb) restricted to the plant transpiration process. Finally, three relationships have been established. The relationships between LAI and NDVI as well as between LAI and K_cb were found both exponential, with associated errors of 30% and 15%, respectively. Because the NDVI saturates at high LAI values (>4), the use of remotely-sensed data results in poor accuracy of LAI estimates for well-developed canopies. However, this inaccuracy was not found critical for transpiration estimates since AET appears limited to ET₀ for well-developed canopies. As a consequence, the relationship between NDVI and K_cb was found linear and of good accuracy (15%).Based on these relationships, maps of LAI and transpiration requirements have been derived from two Landsat7-ETM+ images acquired at the beginning and the middle of the agricultural season. These maps show the space and time variability in crop development and water requirements over a 3 km × 3 km irrigated area that surrounds the fields of study. They may give an indication on how the water should be distributed over the area of interest in order to improve the efficiency of irrigation. The availability, in the near future, of Earth Observation Systems designed to provide both high spatial resolution (10 m) and frequent revisit (day) would make it feasible to set up such approaches for the operational monitoring of crop phenology and irrigation at a regional scale.     

遥感技术在水文水资源领域中的应用研究进展

遥感数据具有周期短、信息量大和成本低的特点,为水文水资源研究提供了丰富的数据源。详细介绍了遥感技术在水文水资源领域的主要应用研究进展,包括降水、蒸散发、径流与水文模型、地表特征及参数提取、洪涝灾害监测、水土保持、土壤水分与旱情监测、积雪与融雪、水质监测和地下水等方面,展望了今后应用研究的发展方向。     

Modelling the effect of canal bed elevation on seepage and water table rise in a sand box filled with loamy soil

The HYDRUS 2D finite difference two-dimensional water balance model was experimentally tested for transient and steady state seepage flux, mound height, and piezometric water level from soil surface as a function of time and horizontal distance from the centre of the canal (half width = 45 cm) under different canal bed elevations (20, 0, −40, −80 and −120 cm denoted as experiments D1, D2, D3, D4 and D5, respectively) and constant water head of 5 cm in a sand box (200 cm × 170 cm × 150 cm) filled with Hisar loam soil. Differences of means between measured and predicted values of infiltration flux, seepage flux and mound height as tested by paired t test were not found significant (P = 0.05). Seepage flux and mound height increased with increasing canal bed elevation. Phreatic level depths were everywhere much shallower than the piezometric water level depths in experiments D1, D2 and D3. However, in experiments D4 and D5 both phreatic and piezometric levels were at similar depths. The seepage parameters and mound height increased, and water table depth decreased, linearly with increasing canal bed elevation. Lowering the canal bed to 120 cm below the soil surface reduced the seepage rate to that of lined canals. The projections in a large flow domain also revealed that lowering the canal to −2 and −4 m below soil surface stabilized the water table at 2.5 and 4.5 m below soil surface, respectively. The practical implications are that open drains should be used for irrigation in areas underlain with a brackish groundwater aquifer and gravity canals may be allowed only where groundwater aquifer is of good quality and sub-surface water withdrawal is practiced for irrigation.     

Model to assess water movement from a shallow water table to the root zone

UPFLOW is a simple software tool developed to estimate with limited data availability and appropriate assumptions the expected upward water movement from a shallow water table to the root zone during a specific period (typically 10-day) in a specific environment. The program contains various sets of soil water retention curves that are considered as representative for various soil classes and indicative values for root water extraction for a number of crops. The environmental conditions are specified in fields of a spreadsheet type Main Menu by specifying: (i) the average evapotranspiration (ET) demand of the atmosphere during the period under consideration, (ii) the expected soil wetness in the topsoil as a result of rain during that period, (iii) the depth of groundwater below the soil surface, (iv) the water extraction pattern of the plant roots, (v) the thickness and characteristics of successive layers of the soil profile and (vi) the salt content of the water table. A steady state upward flow is assumed during the period. The simulations are in line with indicative values presented in literature. Additionally, the software displays the deficient aeration conditions in the root zone and its effect on crop evapotranspiration when the groundwater is close to the soil surface.The model was used to estimate the capillary rise from shallow groundwater (1–1.5 m) to the root zone (0.4–0.6 m) of horticultural crops in loamy sand and sandy loam soils in Belgium. The field measurements confirm that UPFLOW simulates the correct order of magnitude of the capillary rise to the root zone.UPFLOW is public domain software and hence freely available. An installation disk and manual can be downloaded from the web.     

Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid U.S.

SEBAL and METRIC remote sensing energy-balance based evapotranspiration (ET) models have been applied in the western United States. ET predicted by the models was compared to lysimeter-measured ET in agricultural settings. The ET comparison studies showed that the ET estimated by the remote sensing models corresponded well with lysimeter-measured ET for agricultural crops in the semi-arid climates. Sensitivity analyses on impacts of atmospheric correction for surface temperature and albedo showed that the internal calibration procedures incorporated in the models helped compensate for errors in temperature and albedo estimation. A repeatability test by two totally independent model applications using different images, operators and weather datasets showed that seasonal estimations by the models have high repeatability (i.e. stable results over ranges in satellite image timing, operator preferences and weather datasets). These results imply that the SEBAL/METRIC remote sensing models have a high potential for successful ET estimation in the semi-arid United States.