Prediction of large-scale water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks can benefit from the high spatial resolution soil moisture (SM) data of satellite and modeled products because antecedent SM conditions in the topsoil layer govern the partitioning of precipitation into infiltration and runoff. SM data retrieved from Soil Moisture Active Passive (SMAP) have proved to be an effective method of monitoring SM content at different spatial resolutions: 1) radiometer-based product gridded at 36 km; 2) radiometer-only enhanced posting product gridded at 9 km; and 3) SMAP/Sentinel-1A/B products at 3 and 1 km. In this article, we focused on 9-, 3-, and 1-km SM products: three products were validated against in situ data using conventional and triple collocation analysis (TCA) statistics and were then merged with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) were applied for further evaluation of the three SM products, and the maximize- $R$ method was applied to combine SMAP and NoahMP36 SM data. CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance: $R$ and ubRMSD values of the CDF-matched SMAP products were 0.658, 0.626, and 0.570 and 0.049, 0.053, and 0.055 m³/m³, respectively. When SMAP and NoahMP36 were combined, the $R$ -values for the 9-, 3-, and 1-km SMAP SM data were greatly improved: $R$ -values were 0.825, 0.804, and 0.795, and ubRMSDs were 0.034, 0.036, and 0.037 m³/m³, respectively. These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for creating further applications of LSMs with improved accuracy.

中文翻译：

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SMAP 和 Sentinel-1A/B 衍生的土壤水分估计值与美国中大西洋沿岸平原陆面模型输出的评估和组合

预测与水有关的大规模自然灾害，如干旱、洪水、野火、山体滑坡和沙尘暴，可以受益于卫星和模型产品的高空间分辨率土壤水分 (SM) 数据，因为表土层中的先行 SM 条件控制降水分为入渗和径流。从土壤水分主动被动 (SMAP) 中检索的 SM 数据已被证明是一种在不同空间分辨率下监测 SM 含量的有效方法：1) 基于辐射计的产品在 36 公里处网格化；2) 仅辐射计的增强发布产品，网格为 9 公里；3) SMAP/Sentinel-1A/B 产品在 3 公里和 1 公里处。在本文中，我们重点介绍了 9 公里、3 公里和 1 公里 SM 产品：三种产品针对就地数据使用常规和三重搭配分析 (TCA) 统计数据，然后与 Noah-Multiparameterization version-3.6 (NoahMP36) 地表模型 (LSM) 合并。指数滤波器和累积密度函数 (CDF) 用于进一步评估三个 SM 产品，并且最大化- $R$ 方法被应用于结合 SMAP 和 NoahMP36 SM 数据。CDF 匹配的 9、3 和 1 公里 SMAP SM 数据显示出可靠的性能： $R$ CDF 匹配的 SMAP 产品的 ubRMSD 和 ubRMSD 值分别为 0.658、0.626 和 0.570 和 0.049、0.053 和 0.055 m ³ /m ³。当 SMAP 和 NoahMP36 结合时， $R$ - 9、3 和 1 公里 SMAP SM 数据的值得到了极大改进： $R$ -值分别为0.825、0.804和0.795，并且ubRMSD分别为0.034、0.036和0.037 m ³ /m ³。这些结果表明 SMAP/Sentinel 数据在改进区域尺度 SM 估计和创建 LSM 的进一步应用方面的潜在用途，并提高了精度。