Evapotranspiration (ET) estimation is of great importance due to its key role in water resource management. Surface energy modeling tools such as Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration with Internalized Calibration (METRIC), and the Surface Energy Balance System (SEBS) can estimate the amount of evapotranspiration for every pixel of the satellite images. The main objective of this research is evaporation investigation from the freshwater bodies using SEBAL, METRIC, and SEBS. For this purpose, the Amirkabir dam reservoir and its nearby agricultural lands in a semi-arid climate were selected and studied from 2011 to 2017 as the study area. The implementations of this study were accomplished on 16 satellite images of Landsat TM5 and OLI. Then, SEBAL, METRIC, and SEBS were implemented on the selected images. Moreover, the corresponding pan evaporate measurements on the reservoir bank were considered as the ground truth data. Regarding to the results, SEBAL is not a reliable method to evaluate freshwater evaporation with the coefficient of determination (R²) of 0.36 and the Root Mean Square Error (RMSE) of 5.1 mm. On the other hand, METRIC with RMSE and R² of 0.57 and 2.02 mm and SEBS with RMSE and R² of 0.93 and 0.62 demonstrated a relatively good performance.

蒸散量（ET）估算由于其在水资源管理中的关键作用而非常重要。表面能建模工具，例如陆地表面能平衡算法（SEBAL），内在标定的蒸发蒸腾量（METRIC）和表面能平衡系统（SEBS）可以估算卫星图像每个像素的蒸发量。这项研究的主要目的是使用SEBAL，METRIC和SEBS对淡水体进行蒸发研究。为此，选择了Amirkabir大坝水库及其附近处于半干旱气候的农田，并于2011年至2017年进行了研究。这项研究的实施是在Landsat TM5和OLI的16幅卫星图像上完成的。然后，对所选图像实施SEBAL，METRIC和SEBS。此外，将储层上相应的蒸发皿蒸发量测量值视为地面真实数据。关于结果，SEBAL并不是用确定系数来评估淡水蒸发的可靠方法（R²）为0.36，均方根误差（RMSE）为5.1毫米。另一方面，具有RMSE和R ²为0.57和2.02 mm的METRIC和具有RMSE和R ²为0.93和0.62的SEBS表现出相对良好的性能。