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Accurate information on the distribution of sensible and latent heat fluxes as well as soil moisture is critical for evaluation of background characteristics. Since these fluxes are subject to rapid changes in time and space, it is nearly impossible to determine their spatial and temporal distributions over large areas from ground measurements alone. Therefore, prediction from remote sensing images is very attractive as it enables extensive area coverage and a high repetition rate. In this study, the Surface Energy Balance Algorithm for Land as implemented at New Mexico Tech (SEBAL NM ) is used to estimate sensible and latent heat fluxes in the White Volta Basin of Ghana, West Africa. The objectives are (i) to demonstrate a SEBAL NM application in a part of the world were ground measurements are very scarce and (ii) to compare evapotranspiration (ET) maps obtained from Landsat and MODIS imagery, respectively. The results of this study demonstrate that SEBAL NM can be applied for mapping sensible and latent heat fluxes as well as soil moisture over areas where few or no ground measurements are available using common satellite products (Landsat and MODIS).

13 Pags.- 1 Tabl.- 6 Figs. This paper was presented at the River Basin Management 2005 conference in Bologna, Italy., An assessment of water management in the Flumen District, Central Valley of the Ebro River Basin in Spain, using the remote sensing technique Surface Energy Balance Algorithm for Land (SEBAL) was performed. This assessment was based on the estimation of the actual ET (ETa) to compute net water volumes (Vn). This work extended the analysis by also computing net irrigation volumes (Vi) by introducing a water application efficiency as a function of morphopedologic units (Eam). Two approaches were adopted for SEBAL Vi: a) the crop water demands including months outside the crop season (SEBAL_F1) and b) the crop water demands of the six main crops only in the growing season (SEBAL_F2). The comparison analyses for SEBAL_F1 and SEBAL_F2 Vi show a very good agreement with a bias of 0.09 hm3 and 0.56 hm3, respectively. As a result of an accurate estimation of Vi, the water use efficiency (Ea) for the whole Flumen District was determined to be from 80% to 90%. These are actual figures, thus it is possible to review the current crop and water management, identifying the possible causes for low irrigation efficiency on some plots., The authors would like to thank Dr. Allen and Dr. Bastiaanssen for their support during the study. The authors gratefully acknowledge the financial support of DGAPA-UNAM and CONACyT, Mexico.

Above-average rainfall in the last twelve years has led to rising water levels in the Devils Lake basin in northeastern North Dakota. An irrigation test project has been started at ten farmercooperator field sites within the basin to estimate how much additional water can be utilized via irrigation of agricultural crops. Comprehensive soil/water compatibility investigations coupled with GIS analysis of detailed soil survey data (SSURGO) will be utilized to assess the potential for sustainable irrigation development and potentially extend the results to the entire basin. The installation and monitoring phase of the test project will be described in this paper. Water balance monitoring at the sites will include measurements of rainfall and irrigation, soil water content, deep percolation, and ground water level. The surface energy balance algorithm for land (SEBAL) model will be developed in Erdas Imagine/Arc Map interface for estimating evapotranspiration (ET) in the basin. Correlations will be developed between ET and soil physical and chemical properties affecting sustainability as well as other factors, such as crop types, soil map units, and landscape position in order to assess the feasibility, impact, and sustainability of the larger scale irrigation in this mostly non-irrigated basin.

Although the use of water productivity is gaining importance for evaluating good management practices, there is no standardized framework that aids the calculations. A methodology has been developed to quantify spatial variation of crop yield, evapotranspiration (ET) and water productivity (WPET) using the SEBAL algorithm and high and low resolution satellite images. SEBAL estimated ET was validated over a wheat dominated area in the Yaqui Valley, Mexico and proved to be accurate. Estimated average wheat yields of 5.5 ton ha−1 were well within the range of measured yields reported in the literature. Area average WPET was 1.37 kg m−3 and could be considered high compared to other irrigated systems around the world It is concluded that the proposed methodology is accurate and that better knowledge of the spatial variation of WP provides valuable information for regional water managers to seek local solutions for boosting the productivity of agricultural water use by means of sharing and exchanging good management practices.

Both one‐source and two‐source parameterizations of surface sensible heat flux exchange using radiometric surface temperature have been proposed. Although one‐source algorithms may provide reliable heat fluxes, they often require field calibration and hence are unable to accommodate the diverse range of surface conditions often encountered over a landscape. Two‐source models require fewer assumptions and no a priori calibration, and therefore have a wider range of applicability without requiring any additional input data. A one‐source scheme (SEBAL) that performs an “internal calibration” using hydrologic extremes (hot/dry and wet/cool pixels) encountered within a remote sensing scene has been proposed as a way to eliminate a priori calibration and the need for ancillary data. In this paper, some of the key assumptions in SEBAL are evaluated using a two‐source model, field data, and simulations from a complex soil‐vegetation‐atmosphere transfer (SVAT) model, Cupid.

This paper explores the feasibility of the use of two energy balance models for the estimation of evapotranspiration from satellite data in a large irrigated area. The Rio Guarico irrigation system is located in the State of Guarico in Venezuela. A large water reservoir stores the water coming from the Guarico River and provides water for irrigation to an area of near 60,000 hectares. Rice is the main crop grown in the area.One of the models used in this study was SEBAL (Surface Energy Balance Algorithm for Land), which is an image‐processing model for calculating evapotranspiration (ET) as a residual of the surface energy balance. SEBAL was developed in the Netherlands by Bastiaanssen and has been applied in many developing countries. SEBAL uses satellite data collected by the LANDSAT Thematic Mapper (TM) or other satellite sensors collecting visible, near‐infrared and thermal infrared radiation. The main advantage of SEBAL is the need of a minimum amount of ground data. The second model applied was METR...

The upper Rio Chagres basin is a part of the Panama Canal Watershed. The least known water balance component of this watershed is evapotranspiration. Measurements of actual evapotranspiration rates on the ground are difficult and expensive. The objective of this study is to demonstrate a new inexpensive method for determination of regional evapotranspiration in the watershed. The method uses LANDSAT satellite images that are analyzed using the Surface Energy Balance Algorithms for Land (SEBAL). We use an image from March 27, 2000, for estimation of the distribution of the regional actual evapotranspiration in the Panama Canal Watershed and surrounding areas.

The evapotranspiration (ET) estimates derived in two ways from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). One of the two models is Surface Energy Balance Algorithm for Land (SEBAL), which calculates the surface energy flux with Minimal collateral datum, while the other is Two Sources Energy Balance (TSEB), which distinguishes the radiative character between soil and vegetation components. Compared with the in situ measurements, both models output the rational results, but their relative sensitivities and biases are different for the same input data. In our research site, the outcome from the SEBAL is better than TSEB.

Quantitative estimates of consumptive water use by crops, landscape, and natural vegetation are necessary for water resource planning. Availability of satellite remotely-sensed data offers an alternative to current estimating techniques. Evapotranspiration (ET) is predicted as a residual of the energy balance by the Surface Energy Balance Algorithm for Land (SEBAL) using routine weather data and satellite sensed thermal radiances. The California Department of Water Resources (CDWR) is required by state law to develop a state-wide water plan every five years. Computations of consumptive water use are an integral part of the water planning process. Remotely-sensed ET computed by SEBAL offers potential advantages compared to the current labor- and data-intensive estimates techniques. For 2002, annual actual ET for California was computed by SEBAL using MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images (1 km by 1 km square pixels) and California Irrigation Management Information System (CIMIS) weather data. The annual ET of irrigated, agricultural lands computed by SEBAL is compared to the CDWR current water plan estimates. The hydrologic implications of these different estimates to the recommendations and options discussed in the plan are explored. The advantages, disadvantages and estimated accuracy of the current ET computation method and the remotely-sensed based SEBAL method are compared and contrasted.

Root zone soil moisture is a dynamic variable subject to rapid changes in time as well as space. Accurate, detailed information on the distribution of soil moisture is difficult to obtain. Ground based methods for the measurement of temporal and spatial changes in root zone soil moisture require much time and effort and, therefore, have limited value for soil moisture monitoring at the regional scale. Existing remote sensing methods use microwaves to measure soil moisture near the soil surface (0-10 cm). In this study, the Surface Energy Balance Algorithm for Land (SEBAL) is applied to a series of LandSat TM optical images to determine the regional distribution of the evaporative fraction. From this, soil moisture conditions are derived using an empirical relationship between evaporative fraction and root zone soil moisture. Ground measurements available in the Middle Rio Grande Basin do not cover a sufficiently wide range of soil moisture values for validation of the soil moisture maps derived from optical satellite imagery. Therefore, we conducted a qualitative validation by comparing predicted soil moisture conditions along three transects perpendicular to the Rio Grande with ground observations. This analysis shows that the remote sensing approach is successful in distinguishing between moist and dry pixels in the Middle Rio Grande Basin. Further field investigation will be required to validate the product quantitatively.

Remotely sensed images of the Earth’s surface provide information about the spatial distribution of evapotranspiration. Since the spatial resolution of evapotranspiration predictions depends on the sensor type; scaling transfer between images of different scales needs to be investigated. The objectives of this study are first to validate the consistency of SEBAL algorithms for satellite images of different scales and second to investigate the effect of up- and down-scaling procedures between evapotranspiration maps derived from LandSat 7 and MODIS images. The results of this study demonstrate: (1) good agreement of SEBAL evapotranspiration estimates between LandSat 7 and MODIS images; (2) up- and down-scaled evapotranspiration maps over the Middle Rio Grande Basin are very similar to evapotranspiration maps directly derived from LandSat 7 and MODIS images.

An evapotranspiration method comparison was carried out by the International Water Management Institute (IWMI, Sri Lanka), at two locations in the Gediz Basin, Turkey, in the period from May to September 1998. In the IWMI study a number of ground-based techniques were compared with results obtained by remote sensing methods. Recently, a search of the satellite active archive yielded over 70 high quality level 1b images from NOAA/AVHRR over the same time period. The processing of these images with the SEBAL algorithm enabled us to build up a detailed time series of sensible and latent heat fluxes for a period of 120 days. In this paper a comparison is made between the sensible and latent heat fluxes determined from the present series of NOAA-14/AVHRR images and the results obtained earlier from various other prediction methods applied during the 1998 IWMI project. Specifically, the NOAA/SEBAL results are assessed against the scintillometer and temperature fluctuation methods. The results show that the NOAA derived evapotranspiration values follow the seasonal irrigation cycle quite well and correspond closely to the Landsat derived values, although they are lower than the results obtained with the traditional crop factor and Penman¿Monteith methods.

Seasonal evapotranspiration is an essential measure to model crop growth and hydrological balances particularly for irrigation agriculture in semi-arid environments. Hydrological models traditionally integrate single-spot measurements of meteorological stations to estimate potential evapotranspiration. During the last years, the application of thermal remote sensing data in combination with meteorological data of soil-vegetation-atmosphere models facilitated the estimation of actual evapotranspiration on a large scale. This study employed multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data to apply the Surface Energy Algorithm for Land (SEBAL) model to the heterogeneous environment of the Khorezm region, Uzbekistan. Further meteorological data was used to extrapolate actual evapotranspiration to seasonal actual evapotranspiration. The validation of the modeled actual evapotranspiration showed acceptable accuracy when compared to the limited point-based ground truth data. The integration of a rule-based land use classification with higher spatial resolution revealed the necessity to include sub-pixel knowledge of land use distribution to interpret the modeling results. First evaluations of the water distribution and consumption situation were achieved by interpretation of modeled seasonal actual evapotranspiration with hydrological GIS information.

The Boise Valley of Idaho, USA, has a semi-arid climate with approximately 20 to 30 cm of precipitation per year. The predominant land use type has been agriculture, with approximately 370,000 irrigated hectares. Over the last 20 years, the valley has grown in population from 257,000 in 1980 to 431,000 in 2000. An important issue facing water planners is water availability in a valley that is changing from mostly agricultural land-use to more urban types of land use. Department planners have gained insight into the problem by combining LULC polygons from aerial photograph interpretation, and evapotranspiration from Landsat TM data as processed through the Surface Energy Balance Algorithm for Land, both for the year 2000. Seasonal evapotranspiration was generated for the period March 15, 2000 to October 15, 2000. The product was an "image" of seasonal evapotranspiration. LULC polygons were overlaid on the evapotranspiration image and the mean evapotranspiration was computed for all polygons of each LULC class. The preliminary results show that "Irrigated Agriculture" used 812 mm of water, "Urban Residential" used 684 mm, and "New Subdivision" used 606 mm. The water use for irrigated agriculture should be regarded more reliable than for the other two classes, although the error rate is probably not great.

Crop coefficients (Kc) are defined as the ratio of actual crop evapotranspiration to the evapotranspiration (Eto) of a grass reference crop, often taken from Penman/Monteith"s methodology. They are used to estimate theoretical crop evapotranspiration (Etc). Actual evapotranspiration is measured in field lysimeters but this lacks acceptability when applied to the field or large irrigation schemes where conditions are very variable. This paper uses the SEBAL method (Bastiaanssen et al., 1998) to determine actual evapotranspiration from satellite images. This is compared with estimated Eto from both Penman/Monteiths methodology and Eto estimated from a SEBAL estimation of reference crop evaporation within a project. The range of Kc values for the cotton crop were then calculated. Crop coefficient Kc maps were made for two irrigation projects. The methodology was applied on a large cotton irrigation scheme (Starikan) southern Kazakhstan. The estimated mean real time Kc value of 1.16 (±1-3% error) was higher than the standard Kc value of 1.1 identified by FAO-56. The methodology was validated on a different date and different irrigation schemes (Chardara) 200 km south Starikan. The methodology is discussed.

SEBAL (Surface Energy Balance Algorithm for Land) is an image-processing model comprised of twenty-five submodels for calculating evapotranspiration (ET) as a residual of the surface energy balance. SEBAL was developed in the Netherlands by Bastiaanssen and has been applied in many developing countries. SEBAL has now been applied with Landsat Thematic Mapper (TM) images in southern Idaho to predict monthly and seasonal ET for water management. The validation of SEBAL on the Snake River Plain of Idaho has centered on the use of two precision weighing lysimeter systems for ET measurement that were in place at Kimberly, Idaho. Because of the relatively small dimensions of the lysimeter fields at Kimberly it was not possible to find a band 6 pixel of the Landsat 5 TM completely inside a lysimeter field. This caused "blurring" or contamination of the band 6 pixel from surface temperatures of surrounding fields that were different from those for the lysimeter field. A procedure was developed to map surrogate pixels that had the same spectral characteristics as the lysimeter fields and use these as surrogates to compare SEBAL ET with lysimeter ET. Results indicate a wide distribution of ET computed by SEBAL for groups of fields that appear spectrally similar to the lysimeter fields. Much of the cause of the variation is probably due to differences in the thermal properties (surface temperatures) of individual fields. This illustrates the utility of applying the energy balance process to predict ET that incorporates surface temperature along with other aerodynamic characteristics of the surface.

Surface energy flux estimates over central Oklahoma, during the summer of 2001, are derived in two different ways from thermal infrared and visible-near infrared ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) 90 m resolution observations. In one approach, surface flux estimates are computed using a two-source energy balance (TSEB) model, which distinguishes between soil and vegetation flux components. The benefit of TSEB is an improved surface representation over sparsely vegetated terrain, as compared to one-layer models. In the other approach, surface flux estimates are computed from the Surface Energy Balance Algorithm for Land (SEBAL) model, which computes meteorological variables using information contained within the spatial variability of convective fluxes. A major benefit of the SEBAL model is that estimates can be obtained solely from remote sensing observations. Both models compare reasonably well with surface flux measurements, but their relative sensitivities and biases are different for the same input data. Sources of these modeling sensitivities and biases are discussed.

SEBAL (Surface Energy Balance Algorithm for Land) is an image-processing model comprised of twenty-five computational submodels that calculates evapotranspiration (ET) and other energy exchanges at the Earth's surface. SEBAL uses digital image data collected by Landsat or other remote sensing satellites measuring thermal infrared radiation in addition to visible and near-infrared. SEBAL was originally developed in the Netherlands by Bastiaanssen and was modified during the Idaho study for application to mountainous terrain and clear, cold lakes. In an application to the Bear River Basin of southeastern Idaho, USA, ET was computed as a component of the surface energy balance on a pixel-by-pixel basis. ET for periods in between satellite overpasses was computed using ratios of ET from SEBAL to reference ET computed using data from ground-based weather stations. These ratios were essentially customized "crop coefficients" that were determined uniquely for each pixel of an image. This initial application and testing of SEBAL in Idaho indicates substantial promise as an efficient, accurate, and inexpensive procedure to predict the actual evaporation fluxes from irrigated lands throughout a growing season. Predicted ET has been compared with ground measurements of ET by lysimeter with good results, with monthly differences averaging /spl plusmn/16%, but with seasonal differences of only 4% due to reduction in random error. ET maps via SEBAL provide the means to quantify, in terms of both the amount and spatial distribution, the ET on a field by field basis within each state. In particular, the Idaho Department of Water Resources (IDWR) will use results to predict total, net depletion of water from the Bear River system resulting from irrigation diversions.

Accurate estimation of spatial crop water consumption is needed to increase crop yield, save water and protect soils from degradation, as well as to protect ground water quality through the optimization of fertilizer intake by the crop. Air temperature is a fundamental climate variable, used in models to estimate reference evapotranspiration (ETo). If air temperature varies considerably in space, then wrong ETo predictions can be made for an irrigation district when using data from a single weather station. In this paper, spatial apparent air temperatures were obtained using remotely sensed inputs and ground measurements. The USU airborne video/radiometer multispectral remote sensing system and an Inframetrics 760 scanner were used to obtain high resolution remotely sensed imagery. Data were acquired in two irrigation district locations. One site was located in Smithfield (Utah), and the other in Maricopa (Arizona). A Bowen ratio energy balance system (BR) was located on a wheat field (Smithfield) and an eddy correlation (EC) system on a cotton field (Maricopa). The remote sensing energy fluxes closely matched the measured fluxes, predicting the correct air temperature for the system locations. In both cases the air temperature registered by the energy flux systems, at the time of the flight overpass, was only representative for half of the field. The wheat field with an area of 2.2 ha showed an air temperature variation (.T) of 3 oC, and the cotton field with 5.3 ha showed a .T of 6 oC. Thus, estimating crop water demand, based on a single weather station and using the reference evapotranspiration method, could overestimate (for some parts of the fields) and underestimate (for other parts) the spatial crops water needs. The DT methodology of SEBAL was tested with the imagery. The results indicate that the coldest pixel has to be substituted by the average crop canopy temperature for the field (irrigated) when computing DT. In light of these findings, it is suggested that accurate spatial estimation of the different variables involved in computing ETo should be investigated through remote sensing and GIS techniques. Widely used ETo equations like the FAO Penman-Monteith and Hargreaves 1985 can be utilized for spatial predictions once their variables have been validated. These variables are Tmax, Tmin (daily), Rn (daily net radiation), G (daily soil heat flux), U (average daily wind velocity spatial distribution), etc. In the case of the energy fluxes, methodologies are needed to accurately integrate the one time (snapshot) remote sensing values to daily values.

International audience; A spatialized surface energy balance model was validated over the database acquired in the framework of the ReSeDA program. The benefit of the SEBAL model we considered was to compute wind speed and air temperature using the information contained in the spatial variability of convective fluxes. The multitemporal database allowed performing a validation over cycles of several crops. Problems induced by mixed pixels were reduced using high spatial resolution remote sensing data. We verified the validity of the model basic assumption, i.e. the simultaneous presence of partial areas with very high and very low evaporation rates, and the resulting relation between surface temperature and albedo. Besides, the model provided estimates of wind speed and air temperature close to the field references. The validation of soil heat flux showed the inadequacy of the empirical relationship used through a significant underestimation of the references. The validation of sensible heat flux provided similar results as compared to previous studies that dealt with model validations over databases including numerous situations.; Nous avons validé un modèle de bilan d'énergie de surface en utilisant la base de données ReSeDA. L'intérêt du modèle SEBAL était d'estimer la vitesse du vent et la température de l'air à partir de l'information contenue dans la variabilité spatiale des flux convectifs. La base de données multitemporelle permettait d'effectuer une validation en considérant les cycles de plusieurs cultures. Les problèmes dûs aux pixels mixtes étaient réduits par l'utilisation de données télédétectées à haute résolution spatiale. Nous avons pu vérifier la validité de l'hypothèse de base du modèle, i.e. la présence simultanée de zones très évaporantes ou peu évaporantes, ainsi que la relation entre albedo et température qui en résulte. De plus, le modèle a fourni des estimations de la vitesse du vent et de la température de l'air proche des mesures de référence. La validation du flux de chaleur dans le sol a montré que la relation empirique générait une sous-estimation importante. La validation du flux de chaleur sensible a fourni des résultats comparables à ceux rencontrés dans la littérature et concernant la validation de modèles sur des bases de données incluant de nombreuses situations.

A Surface Energy Balance System (SEBS) is proposed for the estimation of atmospheric turbulent fluxes and evaporative fraction using satellite earth observation data, in combination with meteorological information at proper scales. SEBS consists of: a set of tools for the determination of the land surface physical parameters, such as albedo, emissivity, temperature, vegetation coverage etc., from spectral reflectance and radiance measurements; a model for the determination of the roughness length for heat transfer; and a new formulation for the determination of the evaporative fraction on the basis of energy balance at limiting cases. Four experimental data sets are used to assess the reliabilities of SEBS. Based on these case studies, SEBS has proven to be capable to estimate turbulent heat fluxes and evaporative fraction at various scales with acceptable accuracy. The uncertainties in the estimated heat fluxes are comparable to in-situ measurement uncertainties. Keywords: Surface energy balance, turbulent heat flux, evaporation, remote sensing

Estudos sobre o balanço de radiação em áreas de expansão agropecuária, especialmente em estados agroexportadores como Mato Grosso, são relevantes para a compreensão da interação atmosfera-biosfera. Este estudo estimou alguns componentes do balanço de radiação em áreas distintas (floresta secundária, reflorestamento com eucaliptos e cultivos agrícolas) no município de Lucas do Rio Verde/MT com base em magens do satélite Landsat 5 - TM. As imagens escolhidas foram de 20/01 e 26/04 do ano de 2010. As imagens foram processadas utilizando o algoritmo SEBAL para o cálculo dos valores do saldo de radiação instantâneo (Rn inst), albedo e NDVI. Os resultados evidenciaram alta variabilidade no Rn inst, albedo e NDVI tanto entre as distintas coberturas quanto para as diferentes datas. As áreas de floresta secundária e reflorestamento de eucaliptos apresentaram magnitudes similares, enquanto as áreas com cultivos agrícolas os valores apresentam relações intrínsecas referentes aos estádios de desenvolvimento com complexa interação radiativa. Studies on the radiation balance in areas of agricultural expansion, especially in agro-exporting states such as Mato Grosso, are relevant to understanding the atmosphere-biosphere interaction. This study estimated some components of the radiation balance in different areas (secondary forest, reforestation with eucalyptus trees and agricultural crops) in the city of Lucas do Rio Verde/MT using Landsat 5 - TM images. The images chosen were acquired on January 20 and April 26 of 2010. The images were processed using the SEBAL algorithm to calculate the values of the instantaneous radiation balance (Rn inst), albedo and NDVI. The results showed high variability of Rn inst, albedo and NDVI considering both different coverages and dates. The areas of secondary forest and reforestation of eucalyptus showed similar magnitudes, while areas with agricultural crops had values with intrinsic relations for the stages of development with complex radiative interaction.

SEBAL (Surface Energy Balance Algorithm for Land) is an image-processing model comprised of twenty-five submodels that calculates evapotranspiration (ET) and other energy exchanges at the earth’s surface. SEBAL uses image data from Landsat or other satellites measuring thermal infrared radiation, visible and near-infrared. SEBAL was developed in the Netherlands by Bastiaanssen and was modified during this study for mountainous terrain and clear, cold lakes. ET is computed on a pixel-by-pixel basis from an energy balance. The Bear River Basin covers 20,000 km 2 of Idaho, Utah, and Wyoming and contains about 190,000 ha of crop land. ET maps were generated on a monthly basis. The maps show a progression of ET during the year as well as distribution in space. Predicted ET was compared with lysimeter measurements of ET with good results, with monthly differences averaging +/- 16%, but with seasonal differences of only 4% due to reduction in random error.

A small field campaign was conducted during August 1997 in the Piano di Rosia area in Tuscany, Italy. The terms of both the radiation balance and the surface energy balance were measured by several techniques and for several field sites. Together with a LANDSAT-TM scene of 23 August 1997 of the same area, these data give an excellent opportunity for a profound study of interaction between the radiation and energy fluxes from point to regional scale. A new method to derive the surface energy fluxes from remote sensing measurements, called the Simplified Surface Energy Balance Index (S-SEBI), is developed, tested and validated with the available data. If the atmospheric conditions over the area can be considered constant and the area reflects sufficient variations in surface hydrological conditions the fluxes can be calculated without any further information than the remote sensing image itself. The results of this case study show that with the relative simple S-SEBI method the surface energy balance parameters can be estimated with a high precision. The measured and estimated evaporative fraction values have a maximum relative difference of 8%.

In this paper are investigated the net radiation (Rn) and the potential glacier melt discharge trends in a glacier in the tropical Andes, for the assessment of climate change impacts on the water resources availability in remote regions. It is assessed the applicability of remote sensing techniques, through the performance evaluation of the Surface and Energy Balance algorithm (SEBAL). For the calibration it is used ground data observed on the ablation surface of the Zongo glacier in Bolivia. The potential climate change impacts quantified are on the glacier melt discharge. The inferences are calibrated in the period 2004-2007. Results show that the SEBAL performance is adequate from an engineering perspective (Root Mean Square Error for albedo estimations are 0.15, in average). Climate change impacts for the period 1986-2005 are an increase of 42% in the Rn, and the loss in the glacier ablation area of 37% (both in reference to 1986). The melt from the studied glacier in the period 2004-2007 is estimated to produce a maximum potential energy of 3.1 [MW h] in average per day (dry season), which in the practice can be used to quantify the potential impacts of the climate change in partially glacierized catchments.

The general aim of this paper is to demonstrate that remote sensing techniques may easily provide the information needed for the application, over large areas, of agro-hydrological models for the actual evapotranspiration estimation. The application of the SEBAL model has been investigated at field scale and at district scale using highresolution hyperspectral (VIS/NIR + TIR) airborne data acquired in a Sicilian test area. In a first phase, SEBAL has been validated by means of scintillometer flux measurements. Then an application over a district area using MIVIS airborne data has been performed. The procedure provides the actual daily evapotranspiration distribution for a set of irrigated arboreous crops, confirming the potential advantage of this approach as an operative future tool useful to improve the efficiency of irrigation systems.

[1] The irrigated Indus Basin in Pakistan has insufficient water resources to supply all its stakeholders. Information on evaporative depletion across the Basin is an important requirement if the water resources are to be managed efficiently. This paper presents the Surface Energy Balance Algorithm for Land (SEBAL) method used to compute actual evapotranspiration for large areas based on public domain National Oceanic and Atmospheric Administration (NOAA) satellite data. Computational procedures for retrieving actual evapotranspiration from satellites have been developed over the last 20 years. The current work is among the first applications used to estimate actual evapotranspiration on an annual scale across a vast river basin system with a minimum of ground data. Only sunshine duration and wind speed are required as input data for the remote sensing flux algorithm. The results were validated in the Indus Basin by comparing results from a field-scale transient moisture flow model, in situ Bowen ratio measurements, and residual water balance analyses for an area of 3 million ha. The accuracy of assessing time-integrated actual annual evapotranspiration varied from 0% to 10% on a field scale to 5% at the regional level. Spatiotemporal information on actual evapotranspiration helps to evaluate water distribution and water use between large irrigation project areas. Wide variations in evaporative depletion between project areas and crop types were found. Satellite-based measurements can provide such information and avoid the need to rely on field databases.

Estimation of evapotranspiration is a key factor to achieve sustainable water management in irrigated agriculture because it represents water use of crops. Satellite-based estimations provide advantages compared to direct methods as lysimeters especially when the objective is to calculate evapotranspiration at a regional scale. The present study aimed to estimate the actual evapotranspiration (ET) at a regional scale, using Landsat 8 - OLI/TIRS images and complementary data collected from a weather station. SEBAL model was used in South-West Paraná, region composed of irrigated and dry agricultural areas, native vegetation and urban areas. Five Landsat 8 images, row 223 and path 78, DOY 336/2013, 19/2014, 35/2014, 131/2014 and 195/2014 were used, from which ET at daily scale was estimated as a residual of the surface energy balance to produce ET maps. The steps for obtain ET using SEBAL include radiometric calibration, calculation of the reflectance, surface albedo, vegetation indexes (NDVI, SAVI and LAI) and emissivity. These parameters were obtained based on the reflective bands of the orbital sensor with temperature surface estimated from thermal band. The estimated ET values in agricultural areas, native vegetation and urban areas using SEBAL algorithm were compatible with those shown in the literature and ET errors between the ET estimates from SEBAL model and Penman Monteith FAO 56 equation were less than or equal to 1.00 mm day-1. RESUMO A Estimativa da evapotranspiração é um fator chave para alcançar uma gestão sustentável da água na agricultura irrigada, pois representa o uso consuntivo da água dos cultivos agrícolas. Estimativas baseadas em imagens de satélite oferecem vantagens em comparação aos métodos diretos como lisímetros, especialmente quando o objetivo é calcular a evapotranspiração em escala regional. O presente estudo teve como objetivo estimar a evapotranspiração real (ETr) em escala regional, utilizando imagens Landsat 8 - OLI/TIRS e dados complementares coletados de estação meteorológica. O modelo SEBAL foi aplicado no Sudoeste do Paraná, região composta por áreas agrícolas irrigadas e sequeiro, vegetação nativa e áreas urbanas. Cinco imagens Landsat 8, órbita/ponto 223/78, dia do ano 336/2013, 19/2014, 35/2014, 131/2014 e 195/2014, foram utilizadas a partir da qual a ETr em escala diária foi estimada como parte residual do balanço de energia de superfície para produzir mapas de ETr. Os procedimentos para obter ETr usando SEBAL incluem calibração radiométrica, cálculo da refletância, albedo da superfície, índices de vegetação (NDVI, SAVI e IAF) e emissividade. Estes parâmetros foram obtidos com base nas bandas reflectivas do sensor orbital com a superfície temperatura estimada utilizando a banda térmica. Os valores de ETr estimados em áreas agrícolas, vegetação nativa e áreas urbanas utilizando o algoritmo SEBAL foram compatíveis com os apresentados na literatura e o erro entre evapotranspiração estimada com o SEBAL e a equação de Penman Monteith FAO 56 foram inferiores ou iguais a 1,00 mm dia- 1.

Irrigated agricultural sector meets challenge in using water efficiently and effectively while improving food acquisition. One of the strategies to increase food production dealing with water scarcity is by increasing crop water productivity. Spatial data of crop water productivity parameters on large area is needed to support better water resources management. Remote sensing provides viable means to meet requirement of improved regional-scale data set of crop water productivity, such as crop water consumption (evapotranspiration) and crop yield. A methodology is developed to quantify spatial variation of seasonal evapotranspiration (ET) of lowland rice in paddy field with heterogeneous cropping calendar using satellite data. The methodology is developed by synthesizing SEBAL (Surface Energy Balance Algorithm for Land) method and lowland rice phenology detection method. Study area is located in the northern part of West Java Province, known as Pantura Jawa Barat region. The developed method is able to describe spatial variability of lowland rice evapotranspiration in Pantura Jawa Barat region for the growing period of dry season year 2004. The average seasonal actual evapotranspiration of rice is found to be 274.5 mm/season with standard deviation ± 44.7 mm/season.

Over consumption of water resources due to unplanned and inappropriate irrigation methods would be a threat to the ecosystem health and to the sustainable agriculture in Vavuniya District. The objective of this study was to identify the most appropriate irrigation method using remotely sensed or derived hydro geological and meteorological information. The impact of different types of irrigation methods on water use efficiency, water consumption, socio economic aspects of irrigation and soil condition were considered to fulfil the objective of this study. Evaporative fraction, soil moisture content, hydrological parameters and the meteorological parameters were derived using satellite imageries namely the products of Moderate Resolution Imaging Spectro-radiameter (MODIS), MOD 09 A1 (Solar Zenith Angle), MOD 11 A1 (Land Surface Temperature), MOD 13 A1 (Normalized Different Vegetation Index) and MOD 43 B3 (Surface Albedo) and the Shuttle Radar Topography Mission (SRTM) DEM (90m resolution) associated with field reference data during January 2014 to June 2014. Estimation of evaporative fraction and soil moisture content were collectively done with the aid of Surface Energy Balance Algorithm (SEBAL) methodin Ilwis 3.7 environment. Appropriate geometric corrections and image processing operations were applied. Parameterization of hydrological and meteorological parameters was done using automated operations associated with auxiliary information of respective imageries. Information on socio economic aspects of irrigation methods were gathered from semi structured interviews and secondary data sources. This study revealed that integral of hydrological, topographical, meteorological and socio economic factors signifying the importance of micro irrigation methods as viable and effective irrigation method in agriculture. Encouraging the farmers to use micro irrigation methods by providing the credit facilities and the subsidy to install micro irrigation systems is vital for the water resource conservation and thereby the sustainable agriculture.

Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change.

Actual evapotranspiration (ETa) is one of the most useful indicators to explain whether the water is being used as “intended”. ETa variations, both in space and time and for different land use types are seen to be highly indicative for the adequacy, reliability and equity in water use; the knowledge of these conditions is essential for judicious water resources management. Unfortunately, ETa estimation under actual field conditions is still a big challenge for both scientists and water managers. The complexity associated with the estimation of ETa has led to the development of various methodological approaches for estimating ETa over time. During the past two to three decades, significant progress has been made in estimating actual evapotranspiration using satellite remote sensing. These methods provide powerful means for computing ETa from a pixel scale to that of an entire basin. In this study, surface energy balance algorithm for land (SEBAL) was used to compute a complete radiation and energy balance along with the resistances for momentum, heat and water vapor transport for each pixel in the Upper Ewaso Ng’iro North River Basin, in Kenya. This was then applied to assess the spatio-temporal distribution of ETa in the basin. The mean annual ETa estimated from SEBAL for 2000, 2003 and 2006 were compared with the mean annual ETa calculated from water balance method for the same periods and a good correlation of about 70% was observed. It was further observed that ETa increased gradually from 2000 to 2006 with an annual rate of about 15%. The estimated daily, monthly and annually ETa distribution for the period of study were used to analyze water use patterns across the basin thus giving more insights into the underlying factors impacting on the water resources which could be used to facilitate the formulation of appropriate water resources management strategies for the basin.

Remote sensing-based evapotranspiration (ET) algorithms developed in recent years are well suited for estimating evapotranspiration and its spatial trends over time. In this paper the application of energy balance methods in South Africa is reviewed, showing that the Surface Energy Balance Algorithm for Land (SEBAL) model is the most widely used, but highlighting the potentials of the Surface Energy Balance System (SEBS) model. The SEBS model is then reviewed in the international literature and lessons learned from South African examples are expanded upon. The SEBS model has been extensively used for teaching and training purposes and has been applied in research projects across many different environments. However, there are discrepancies in the reported accuracy of the SEBS model due to known model sensitivities. It is therefore recommended that any further research using the SEBS model in South Africa should be limited to agricultural areas where accurate vegetation parameters can be obtained, where high resolution imagery with low sensor zenith angles is available, and where canopy cover is complete.

Evapotranspiration (ET) plays a crucial role in integrated water resources planning, development and management, especially in tropical and arid regions. Determining ET is not straightforward due to the heterogeneity and complexity found in real-world hydrological basins. This situation is often compounded in regions with limited hydro-meteorological data that are facing rapid development of irrigated agriculture. Remote sensing (RS) techniques have proven useful in this regard. In this study, we compared the daily actual ET estimates derived from 3 remotely-sensed surface energy balance (SEB) models, namely, the Surface Energy Balance Algorithm for Land (SEBAL) model, the Operational Simplified Surface Energy Balance (SSEBop) model, and the Simplified Surface Balance Index (S-SEBI) model. These products were generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a total of 44 satellite overpasses in 2005, 2010, and 2015 in the heterogeneous, highly-utilized, rapidly-developing and data-limited Kilombero Valley (KV) river basin in Tanzania, eastern Africa. Our results revealed that the SEBAL model had a relatively high ET compared to other models and the SSEBop model had relatively low ET compared to the other models. In addition, we found that the S-SEBI model had a statistically similar ET as the ensemble mean of all models. Further comparison of SEB models’ ET estimates across different land cover classes and different spatial scales revealed that almost all models’ ET estimates were statistically comparable (based on the Wilcoxon’s test and the Levene’s test at a 95% confidence level), which implies fidelity between and reliability of the ET estimates. Moreover, all SEB models managed to capture the two spatially-distinct ET regimes in KV: the stable/permanent ET regime on the mountainous parts of the KV and the seasonally varied ET over the floodplain which contains a Ramsar site (Kilombero Valley Floodplain). Our results have the potential to be used in hydrological modelling to explore and develop integrated water resources management in the valley. We believe that our approach can be applied elsewhere in the world especially where observed meteorological variables are limited.

Estimation of crop water deficit continues to be challenging especially when attempting to evaluate it at large areas. The limitation of cloud free images availability can be overcome with the combination of high-spatial and temporal resolution images in the tropical climate. In this study, Landsat 7 & MODIS satellites Image on the same overpass date was selected on May 18, 2001 in the dry season 2001. Comparing positively the results of Modis, and Landsat 7 ETM+ sensors for water consumption studies at different scales In Irrigation system would enable better decision-making for located crop water requirement. The Surface Energy Balance Algorithm for Land (SEBAL) has been applied to Landsat 7 ETM+, and MODIS sensors for the estimation of crop water requirement In District I of the Upper Purnapanga River Integrated Irrigation System (UPRIIS) In Central Luzon of Philippines. The actual evapotranspiration was integrated for 24 hours on pixel-by-pixel basis from the Instantaneous evapotranpiration (ET). This paper shows a unique combination of derived ETa from Landsat and the MODIS images for water consumption studies In District 1. Volumes of water consumption and deficit were compared at different pixel sizes. The results were compared with the evapotranspiration calculations at two meteorological stations In District 1 of UPRIIS. The discussion provides the research orientation of the scale assessment of evaporation and further Implications In applied research for water management aided by satellite Images.

Spatio-temporal distribution of irrigation water components was evaluated at the canal command area in Indus Basin Irrigation System (IBIS) by using a remote sensing-based geo-informatics approach. Satellite-derived MODIS product-based Surface Energy Balance Algorithm for Land (SEBAL) was used for the estimation of the actual evapotranspiration (ETa). The ground data-based advection aridity method (AA) was used to calibrate and validate the model. Statistical analysis of the SEBAL based ETa and AA shows the mean values of 87.1 mm and 47.9 mm during Kharif season (May–November) and 100 mm and 77 mm during the Rabi Season (December–April). Mean NSEs of 0.72 and 0.85 and RMSEs 34.9 and 5.76 during the Kharif and the Rabi seasons were observed for ETa and AA, respectively. Rainfall data were calibrated with the point observatory data of the metrological stations. The average annual ETa was found 899 mm for defined four cropping years (2011–2012 to 2014–2015) with the minimum average value of 63.3 mm in January and the maximum average value of 110.6 mm in August. Average of the sum of net canal water use (NCWU) and rainfall during the study period of four years was 548 mm (36% of ETa). Seasonal analysis revealed 39% and 61% of groundwater extraction proportion during Rabi and Kharif seasons, dependent upon the occurrence of rainfall and crop phenology. Overall, the results provide insight into the interrelationships between key water resources management components and the variation of these through time, offering information to improve the strategic planning and management of available water resources in this region.

The Ethiopian rift is known for its diverse landscape, ranging from arid and semi-arid savannahs to high and humid mountainous regions. Lacustrine sediments and paleo-shorelines indicate water availability fluctuated dramatically from deep fresh water lakes, to shallow highly alkaline lakes, to completely desiccated lakes. To investigate the role lakes have played through time as readily available water sources to humans, an enhanced knowledge of the pace, character and magnitude of these changes is essential. Hydro-balance models are used to calculate paleo-precipitation rates and the potential pace of lake level changes. However, previous models did not consider changes in hydrological connectivity during humid periods in the rift system, which may have led to an overestimation of paleo-precipitation rates. Here we present a comprehensive hydro-balance modelling approach that simulates multiple rift lakes from the southern Ethiopian Rift (lakes Abaya, Chamo and paleo-lake Chew Bahir) simultaneously, considering their temporal hydrological connectivity during high stands of the African Humid Period (AHP, ~15–5 ka). We further used the Surface Energy Balance Algorithm for Land (SEBAL) to calculate the evaporation of paleo-lake Chew Bahir’s catchment. We also considered the possibility of an additional rainy season during the AHP as previously suggested by numerous studies. The results suggest that an increase in precipitation of 20 to 30% throughout the southern Ethiopian Rift is necessary to fill paleo-lake Chew Bahir to its overflow level. Furthermore, it was demonstrated that paleo-lake Chew Bahir was highly dependent on the water supply from the upper lakes Abaya and Chamo and dries out within ∼40 years if the hydrological connection is cut off and the precipitation amount decreases to present day conditions. Several of such rapid lake level fluctuations, from a freshwater to a saline lake, might have occurred during the termination of the AHP, when humid conditions were less stable. Fast changes in fresh water availability requires high adaptability for humans living in the area and might have exerted severe environmental stress on humans in a sub-generational timescale.