To manage inland water resources, surveying the performance of remote sensing models for estimating the actual evaporation in arid regions is so important. Hence, this study aimed to assess the performance of two energy balance algorithms including surface energy balance algorithm for land (SEBAL) and operational simplified surface energy balance (SSEBop) in freshwater and saline water bodies. Another purpose of the present study was efficiency improvement in hypersaline lakes. In this regard, a practical salinity correction coefficient was used to overcome shortcomings of the selected models over saline Lake. The analysis of yearly lake water budget was used to assess the selected energy balance algorithms’ performance with a novel approach. These algorithms were investigated at Shahid Kazemi Dam Reservoir (as a freshwater body) and Urmia Lake (as a hypersaline water body) in Iran. The results showed that two selected algorithms estimated the evaporation rate at the selected freshwater body with a proper accuracy. The results showed the root mean square error for SEBAL result (RMSE_SEBAL) as 2.0 mm/day, correlation coefficient for SEBAL result (R_SEBAL) as 0.80 mm/day, and RMSE_SSEBop and R_SSEBop as 1.7 and 0.80 mm/day, respectively. However, these models overestimated evaporation over the hypersaline water body (RMSE_SEBAL = 88.4 mm/month, R_SEBAL = 0.90 and RMSE_SSEBop = 39.9 mm/month, R_SSEBop = 0.94). Salinity correction coefficient improved the results as RMSE_SEBAL = 19.8 mm/month, R_SEBAL = 0.90 and RMSE_SSEBop = 13.4 mm/month, and R_SSEBop = 0.94. In general, the algorithm performance was improved using the salinity correction coefficient in the chosen hypersaline water body.

为了管理内陆水资源，调查遥感模型的性能以估计干旱地区的实际蒸发量非常重要。因此，本研究旨在评估两种能量平衡算法的性能，包括陆地表面能平衡算法（SEBAL）和操作简化表面能平衡（SSEBop）在淡水和盐水水体中的性能。本研究的另一个目的是提高高盐湖的效率。在这方面，使用实际的盐度校正系数来克服盐湖上所选模型的缺点。年度湖泊水预算的分析被用来通过一种新颖的方法评估所选能量平衡算法的性能。在伊朗的Shahid Kazemi大坝水库（作为淡水体）和Urmia湖（作为超盐水体）研究了这些算法。结果表明，两种选择的算法以适当的精度估算了所选淡水体的蒸发速率。结果显示SEBAL结果（RMSE）的均方根误差_SEBAL）为2.0毫米/天，SEBAL结果的相关系数（R _SEBAL）为0.80毫米/天，RMSE _SSEBop和R _SSEBop分别为1.7和0.80毫米/天。然而，这些模型高估了高盐水体的蒸发（RMSE _SEBAL  = 88.4 mm /月，R _SEBAL  = 0.90和RMSE _SSEBop  = 39.9 mm /月，R _SSEBop  = 0.94）。盐度校正系数改善了结果，因为RMSE _SEBAL  = 19.8 mm /月，R _SEBAL  = 0.90和RMSE _SSEBop  = 13.4 mm /月，以及R _SSEBop = 0.94。通常，在所选的高盐水体中使用盐度校正系数可以提高算法性能。