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The quantification of evapotranspiration from irrigated areas is important for agriculture water management, especially in arid and semiarid regions where water deficiency is becoming a major constraint in economic welfare and sustainable development. Conventional methods that use point measurements to estimate evapotranspiration are representative only of local areas and cannot be extended to large areas because of heterogeneity of landscape. Remote sensing based energy balance models are presently most suited for estimating evapotranspiration at both field and regional scales. In this study, SEBAL (Surface Energy Balance Algorithm for Land), a remote sensing based evapotranspiration model, has been applied with Landsat ETM+ sensor for the estimation of actual evapotranspiration in the Habra plain, a semiarid region in west Algeria with heterogeneous surface conditions. This model followed an energy balance approach, where evapotranspiration is estimated as the residual when the net radiation, sensible heat flux and soil heat flux are known. It involves in the input the remote sensing land surface parameters such as surface temperature, NDVI and albedo. Different moisture indicators derived from the evapotranspiration were then calculated: evaporative fraction, Priestley-Taylor parameter and surface resistance to evaporation. These calculated indicators facilitate the quantitative diagnosis of moisture stress status in pixel basis. The study area contains extremes in surface albedo, vegetation cover and surface temperature. The land uses in this study area consists of irrigated agriculture, rain-fed agriculture and livestock grazing. The obtained results concern the validation of the used model for spatial distribution analysis of evapotranspiration and moisture indicators. The evaluation of daily evapotranspiration and moisture indicators are accurate enough for the spatial variations of evapotranspiration rather satisfactory than sophisticated models without having to introduce an important number of parameters in input with difficult accessibility in routine. In conclusion, the results suggest that SEBAL can be considered as an operational method to predict actual evapotranspiration from irrigated areas having limited amount of ground information.

Obtaining evapotranspiration and surface energy fluxes with remotely sensed data to improve agricultural water management