Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.

中文翻译：

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带袋装和遗传算法的随机森林结合最小修整平方回归使用 SMOS 卫星土壤水分对土壤水分亏缺

土壤水分亏缺 (SMD) 是土壤含水量变化的关键指标，对天气和气候、自然灾害、农业用水管理等多种应用都很有价值。任务侧重于土壤水分反演，可用于 SMD 估计。在这项研究中，使用 SMOS 得出的土壤水分来估计流域尺度的 SMD。此处实现了几种估计 SMD 的方法，使用诸如随机森林 (RF) 和遗传算法以及最小修整平方 (GALTS) 回归之类的算法。结果表明，对于 SMD 估计，RF 算法与 GALTS 相比表现最佳，均方根误差 (RMSE) 分别为 0.021 和 0.024。总而言之，