Development of a new algorithm (SM-SEBAL) to evaluate Evapotranspiration based on remote sensing data

The algorithm of Sufrace Energy BALance (SEBAL) is one of the most widely used methods for determining actual evapotranspiration through remote sensing. However, assuming a linear relationship between temperature difference (dT) and land surface temperature (LST) and the visual identification of hot and cold pixels are the weak points of the method. Modified SEBAL (M-SEBAL) on the other hand, replaces two new concepts as hot and cold edges, with a significant impact on increasing the accuracy and automation of the execution of the algorithm and removing the need for skilled user. Although, determining the warm edge requires high computing and thus increase in computing time and possible errors. In this study, a simplified form of the M-SEBAL (SM-SEBAL) is developed and the results of executing of the three method on MODIS images and comparing the results with the observed data are shown. Results show that SEBAL overstimates the ET values and M-SEBAL and SM-SEBAL underestimate the same values. The highest sensible heat flux calculation error comes from M-SEBAL (33.87%) and SEBAL shows the lowest error rate (11.23%). The highest error in the calculation of the latent heat (LE) retrives from M-SEBAL (11.66%) and the lowest error rate retrives from SEBAL (5.05%). However, the maximum daily evapotranspiration observed error was for SEBAL (3.56%) and the lowest error was retrived from SM-SEBAL (1.81%) and then M-SEBAL (2.47%). In addition, the time required to perform SEBAL algorithm is up to four times more than the time needed to run other two algorithms.