Improved soil evaporation remote sensing retrieval algorithms and associated uncertainty analysis on the Tibetan Plateau

Abstract

Abstract. Actual evapotranspiration (ET) is the key link between water and energy cycles. However, accurate evaporation estimation in alpine barren areas remains understudied. In this study, we aimed to improve the satellite-driven Process-based Land Surface ET/Heat fluxes algorithm (P-LSH) for better satellite retrieval of ET on the Tibetan Plateau by introducing two effective soil moisture constraint schemes in which normalized surface soil moisture and the ratio of cumulative antecedent precipitation to cumulative antecedent equilibrium evaporation are used to represent soil water stress, respectively, based on the intercomparison and knowledge-learning of the existing schemes. We first conducted intercomparison of six existing soil evaporation algorithms and sorted out the two most effective soil moisture constraint schemes. We then introduced the modified versions of the two constraint schemes into the P-LSH algorithm and further optimized the parameters using the differential evolution method. As a result, it formed two improved P-LSH algorithms. We systematically assessed the performances of the two improved P-LSH algorithms and six existing remote sensing ET retrieval algorithms on two barren-dominated basins of the Tibetan Plateau using reconstructed ET estimates derived from the terrestrial water balance method as a benchmark. The two moisture constraint schemes largely improved the performance of the P-LSH algorithm and showed better performance in both basins (root mean square error (RMSE) = 7.36 and 7.76 mm per month; R2=0.86 and 0.87), resulting in a higher simulation accuracy than all six existing algorithms. We used five soil moisture datasets and five precipitation datasets to further investigate the impact of moisture constraint uncertainty on the improved P-LSH algorithm. The ET estimates of the improved P-LSH algorithm, driven by the GLDAS_Noah soil moisture, performed best compared with those driven by other soil moisture and precipitation datasets, while ET estimates driven by various precipitation datasets generally showed a high and stable accuracy. These results suggest that high-quality soil moisture can optimally express moisture supply to ET, and that more accessible precipitation data can serve as a substitute for soil moisture as an indicator of moisture status for its robust performance in barren evaporation.