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Remote sensing provides reliable information for the quantification of evapotranspiration (ET) over large areas, essential for water management and irrigation scheduling. The ET represents the Crop Water Requirements (CWR) that must be provided by rainfall and/or irrigation to ensure the crop yield. During last decades different ET estimation methods were developed according to problem-specific requirements, characteristics of data input (e.g. data accuracy, availability and resolution) and temporal and spatial scale of interest. The selection of the best methodology has a great influence on the results of ET estimation. Generally, the comparison of ET estimated trough different methods is affected by many parameters: data input (different sources, typology, temporal and spatial resolutions), different scales of analysis (from field to global scale), contests, crop type and climate condition. For this reason, defining whether algorithm can capture spatial and temporal pattern of ET at the required accuracy is a significant challenge. In this study two different methods, both based on the logic of the Penman-Monteith equation, were tested for ET trends estimation at irrigation district scale: the improved algorithm of the MODIS ET product (MYD16A2 V006) and the “Analytical Approach”. While the MODIS product follows the energy balance method, the Analytical Approach exploits the single crop coefficient (Kc) approach proposed by the FAO in Irrigation and Drainage Paper No. 56. It combines agrometeorological data measured in situ and surface reflectance satellite derived data: the albedo (α) of the crop-soil surface and the Leaf Area Index (LAI). In order to compare the two ET trends, the satellite data input used in the present work were chosen from the MODIS products: MODIS LAI (MCD15A2H V006) and MODIS Albedo (MCD43A3 V006). The comparison was assessed in the study area of “Sinistra Ofanto” Irrigation district located in the Apulia Region (Italy) and characterized by an extremely heterogeneous and fragmented landscape.
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