Abstract Soil moisture (SM) plays an indispensable role in many practical applications, such as drought monitoring, hydrologic applications and agricultural management. Passive microwave remote sensing has proven capable of capturing changes in SM. However, the coarse spatial resolution (approximately 25–40 km) may greatly limit many regional hydrological and agricultural applications. In this study, we present a SM downscaling method based on the visible and shortwave-infrared (SWIR) remote sensing data toward improved spatial resolution of Soil Moisture Active Passive (SMAP) SM. In the proposed method, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), horizontally polarized Brightness Temperature (TBh), vertically polarized Brightness Temperature (TBv) and SWIR reflectance collected in Inner Mongolia from April to September 2018 and April to September 2019 are used as input data and combined with topographic information to downscale the SMAP SM L3 product from the original 36 km to 1 km. Random forest (RF) was used to link the input data and SMAP SM. Finally, 30 in situ station SM measurements distributed in Inner Mongolia and precipitation data were used to verify the downscaled SM. The results show that the correlation range of the downscaled SM and the in situ SM is 0.246 to 0.739, with an average of 0.535. The root mean square error (RMSE) range is 0.02 to 0.152 m3/m3, and the mean RMSE is 0.059 m3/m3. The ubRMSE has a range of 0.02 to 0.06 m3/m3, and the mean performance is 0.04 m3/m3. The bias ranges from −0.145 to 0.087 m3/m3, and the mean bias is 0.008 m3/m3. The downscaled SMAP SM can capture the spatial heterogeneity of the SMAP SM and the dynamic changes in measured soil moisture. The downscaled SMAP SM also has a high spatial correspondence with the original SMAP SM, providing more detailed soil water information than the original 36 km resolution.

中文翻译：

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基于可见光和短波红外遥感数据的SMAP土壤水分空间降尺度方法

摘要 土壤水分（SM）在干旱监测、水文应用和农业管理等许多实际应用中起着不可或缺的作用。被动微波遥感已被证明能够捕捉 SM 的变化。然而，粗糙的空间分辨率（大约 25-40 公里）可能会极大地限制许多区域水文和农业应用。在这项研究中，我们提出了一种基于可见光和短波红外 (SWIR) 遥感数据的 SM 降尺度方法，以提高土壤水分主动被动 (SMAP) SM 的空间分辨率。在所提出的方法中，地表温度 (LST)、归一化差异植被指数 (NDVI)、水平极化亮度温度 (TBh)、2018 年 4 月至 9 月和 2019 年 4 月至 2019 年 4 月至 9 月在内蒙古收集的垂直极化亮度温度 (TBv) 和 SWIR 反射率作为输入数据，并结合地形信息将 SMAP SM L3 产品从原来的 36 公里缩小到 1 公里。随机森林 (RF) 用于链接输入数据和 SMAP SM。最后，使用分布在内蒙古的 30 个原位站 SM 测量和降水数据来验证缩小的 SM。结果表明，降尺度SM与原位SM的相关范围为0.246～0.739，平均值为0.535。均方根误差 (RMSE) 范围为 0.02 至 0.152 m3/m3，均方根误差为 0.059 m3/m3。ubRMSE 的范围为 0.02 至 0.06 m3/m3，平均性能为 0.04 m3/m3。偏差范围从 -0.145 到 0.087 m3/m3，平均偏差为 0.008 m3/m3。缩小后的 SMAP SM 可以捕捉 SMAP SM 的空间异质性和实测土壤水分的动态变化。缩小后的 SMAP SM 与原始 SMAP SM 也具有较高的空间对应性，提供比原始 36 公里分辨率更详细的土壤水分信息。