Comparison and study of the role of PYSEBAL and SEBS algorithms in estimating actual evapotranspiration in Qazvin plain

There are several methods for monitoring evapotranspiration, which are mainly measured on a point that will be difficult to generalize to the whole area. In recent years, remote sensing-based methods for estimating actual evapotranspiration have been widely considered. In this study, the amount of actual evapotranspiration per day in Qazvin plain was used by SEBS and PYSEBAL algorithms for 15 TM images, 22 ETM + images and 24 MODIS images without clouds and snow during the years 2000 to 2003. The results were compared with a drained lysimeter planted with grass in the Qazvin plain. Comparing the outputs obtained from the two algorithms, it was concluded that the PYSEBAL algorithm, using the latest methods of estimating evapotranspiration, such as the user not selecting two hot and cold pixels and minimal use of ground data, has been able to cover many of the weaknesses of other algorithms, so that the PYSEBAL algorithm in all three MODIS sensors, LANDSAT-ETM+ and LANDSAT-TM respectively with RMSE values (0.45, 0.46 and 2.02 mm/day, and R2 values of 0.96, 0.95 and 0.82) had better performance than SEBS algorithm in the study area. Furthermore, considering that determining the amount of water used for evapotranspiration is one of the most basic factors in planning in order to achieve more product, Kc coefficient can be considered as a suitable and fast guide in irrigation management. Studies on grass plant show the high accuracy of PYSEBAL model in estimating this coefficient. And finally, the use of remote sensing methods can be a good alternative to avoid high costs and improve water management in the region.