Most existing soil moisture (SM) products from earth observations and land surface models over the Qinghai-Tibetan Plateau (QTP) have coarse resolutions or are mostly generated with high spatial resolutions based on downscaling methods. The former could hinder the applications in hydrological and ecological analyses at the regional scale and the performance of the latter could be limited by the intricate relationship between SM and downscaling factors in regions with complex topography. To address this issue, this paper aims to retrieve a 1 km SM product from 2017 to 2021 using Sentinel-1 Synthetic Aperture Radar (SAR) observations based on a semi-empirical method specific to the QTP region (SMS-1) as different from the previous downscaled SM products. The main interest in our retrievals is that the semi-empirical modeling approach allows exploring the relationships between microwave backscatters and the soil and vegetation parameters spatially based on well-defined mathematics. The SMS-1 retrievals were evaluated against the observations from five in-situ networks over the QTP and against six other existing downscaled 1 km SM products. The temporal evaluation against in-situ measurements showed that SMS-1 retrievals performed better than most 1 km SM products obtained from Machine Learning methods (median R = 0.57, ubRMSD = 0.064 m3/m3, RMSD = −0.107 m3/m3 and bias = −0.042 m3/m3) except for SMSg. Furthermore, the SMS-1 retrievals presented reasonable spatial patterns that are consistent with the spatial distribution of the grassland-type map. Our Sentinel-1 SAR-based method can therefore potentially serve as a foundation for the advance of active microwave remote sensing SM algorithm to retrieve spatially high-resolution SM.

中文翻译：

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青藏高原裸土和草地上 Sentinel-1 对 1 km 表层土壤水分的反演


青藏高原 （QTP） 地球观测和地表模型现存的土壤水分 （SM） 产品大多具有粗分辨率，或者大多基于降尺度方法生成，具有较高的空间分辨率。前者可能会阻碍区域尺度的水文和生态分析中的应用，而后者的性能可能会受到 SM 与复杂地形地区降尺度因子之间错综复杂的关系的限制。针对这一问题，本文旨在利用 Sentinel-1 合成孔径雷达 （SAR） 观测数据，基于 QTP 区域特有的半经验方法 （SMS-1） 检索 2017 年至 2021 年的 1 km SM 产品，与以往缩小的 SM 产品不同。我们检索的主要兴趣在于，半经验建模方法允许基于明确定义的数学在空间上探索微波反向散射与土壤和植被参数之间的关系。根据 QTP 上 5 个原位网络的观察结果和其他 6 个现有的缩小 1 km SM 产品对 SMS-1 检索进行了评估。对原位测量的时间评估表明，SMS-1 检索的性能优于从机器学习方法获得的大多数 1 公里 SM 产品（中位数 R = 0.57，ubRMSD = 0.064 m3/m3，RMSD = -0.107 m3/m3 和偏差 = -0.042 m3/m3），除了 SMSg。此外，SMS-1 检索呈现出与草原类型地图空间分布一致的合理空间格局。因此，我们基于 Sentinel-1 SAR 的方法有可能成为主动微波遥感 SM 算法发展的基础，以检索空间高分辨率 SM。