Evapotranspiration estimation over agricultural plains using MODIS data for all sky conditions

Agricultural, water resources and forest management applications require adequate information about evapotranspiration (ET) over a range of spatial and temporal scales. Several methods, with reasonable accuracy, exist for computing ET for clear sky conditions using satellite remote sensing data. However, estimation of ET for cloudy days using remote sensing data remains a challenge. We have developed an ET estimation algorithm for all sky conditions using primarily remote sensing data. The proposed approach is based on an extension of the Priestley-Taylor form of representation, a contextual triangular interpretation of remotely sensed surface temperature and vegetation index, and requires estimation of evaporative fraction, soil heat flux and land surface net radiation that can be derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). For cloudy pixels, a regression-based algorithm utilizes the MODIS cloud data product for cloud top temperature, cloud fraction, cloud emissivity, cloud optical thickness and land surface temperature to calculate net radiation and thereafter ET. The algorithm has been tested and validated with ground observations from South Great Plains in the United States with reasonable accuracy of R2 ranging between 56.7% and 66.9% and an average root mean square of around 70W/m 2, we then, applied the algorithm over the agricultural plains of the Ganges Basin. We anticipate that our algorithm has capabilities to provide a continuous estimation ET, which can then be used for computing several processes such as water stress in croplands over a range of spatial and temporal scales.