Abstract Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications.

中文翻译：

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将超分辨率地表建模与 SMAP 亮度温度相结合，获得 30 米土壤湿度估计值

摘要 准确和详细的土壤水分信息对于灌溉、干旱和洪水预测、水资源管理和田间规模（即数十米）决策至关重要。最近从太空测量土壤水分的卫星任务继续提高土壤水分信息的可用性。然而，这些卫星产品的效用受到微波传感器占用空间大的限制。本研究提出了一个合并框架，该框架结合了超分辨率地表模型 (LSM)、辐射传输模型 (RTM) 和贝叶斯方案，以合并粗分辨率遥感水文变量并将其缩小到 30 米空间分辨率。该框架基于 HydroBlocks，通过相互作用的水文响应单元 (HRU) 解决地表过程的现场尺度空间异质性的 LSM。通过将 HydroBlocks 与土壤水分主动被动 (SMAP) 任务中使用的 Tau-Omega RTM 耦合，该框架针对土壤水分进行了演示。合并来自 HydroBlocks-RTM 和 SMAP L3 的亮温以获得更新的 30 米土壤湿度。我们在美国具有密集的原位土壤水分网络的四个实验流域验证了缩小的土壤水分估计值，并获得了整体高相关性 (> 0.81) 和良好的平均 KGE 分数 (0.56)。在田间和流域尺度上，缩小的产品比单独的 SMAP L3 和 L4 产品更好地捕捉时空土壤水分动态。