Abstract Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPLSM model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle.

中文翻译：

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SMAP 土壤水分改善全球蒸散

摘要 准确估计全球蒸散量（ET）对于理解地球系统中的水循环和陆地-大气反馈至关重要。卫星驱动的 ET 模型提供全球估计，但许多 ET 算法的设计独立于土壤水分观测。由于 ET 的水来自土壤，将土壤湿度纳入 ET 的全球遥感算法理论上应该可以提高性能，尤其是在缺水地区。本文介绍了对广泛使用的 Priestley Taylor-Jet 推进实验室 (PT-JPL) ET 算法的更新，以将空间明确的每日表层土壤水分控制对土壤蒸发和冠层蒸腾作用结合起来。更新后的算法使用 14 个 AmeriFlux 涡流协方差塔进行评估，这些塔与 COsmic 射线土壤水分观测系统 (COSMOS) 土壤水分观测位于同一位置。新的 PT-JPLSM 模型显示出减少的错误和增加的方差解释，在缺水地区的改进最大。土壤水分纳入土壤蒸发通过将偏差和 RMSE 分别降低 29.9% 和 22.7% 来提高 ET 估计值，而将土壤水分纳入蒸腾作用通过将偏差降低 30.2% 和 RMSE 16.9% 来提高 ET 估计值。我们使用来自土壤湿度主动被动任务 (SMAP) 的土壤湿度观测值在全球范围内应用该算法。这些新的全球 ET 估计显示在更精细的空间分辨率下误差减少，并提供丰富的数据集来评估地表和气候模型，