The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address these issues, this paper presents a physics-constrained gap-filling method, PhyFill for short. The PhyFill method employs a partial convolutional neural network technique to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous spatially daily soil moisture data on a global scale. Three validation strategies are employed: visual inspection through global pattern, simulated missing-region validation, and soil moisture validation with measurements. The results indicated that the reconstructed soil moisture achieved a higher spatial coverage with satisfactory spatial continuity with neighbouring pixels. The simulated validation of the missing regions revealed that the averaged unbiased root mean square difference (ubRMSD) and correlation coefficient (R) were 0.01 m/m and 0.99, respectively the gap filled SMAP product. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSD compared with the original SMAP soil moisture data (0.04 m/m 0.04 m/m). The PhyFill method can generate globally continuous, high accurate soil moisture estimates, providing remarkable support for advanced hydrological applications, , global soil moisture dry-down events and patterns.

中文翻译：

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通过深度学习中的物理耦合来弥合全球土壤湿度绘图中的时空不连续性


2015年发射的土壤湿度主动被动（SMAP）卫星在全球土壤湿度测绘方面取得了重大成果。然而，由于轨道扫描间隙和反演算法的限制，土壤水分产品出现了时空不连续性。为了解决这些问题，本文提出了一种物理约束的间隙填充方法，简称PhyFill。 PhyFill 方法采用部分卷积神经网络技术来探索原始 SMAP 土壤湿度数据的空间域特征。然后，它将降水事件和干旱事件引起的土壤水分变化作为损失函数中的惩罚项，从而解释控制土壤水分动态波动的物理过程中的单调性和边界约束。 PhyFill 模型应用于 SMAP 土壤湿度数据，产生全球范围内连续的空间日土壤湿度数据。采用三种验证策略：通过全局模式进行目视检查、模拟缺失区域验证以及通过测量进行土壤湿度验证。结果表明，重建的土壤湿度具有较高的空间覆盖率，与相邻像素具有良好的空间连续性。缺失区域的模拟验证表明，间隙填充 SMAP 产品的平均无偏均方根差 (ubRMSD) 和相关系数 (R) 分别为 0.01 m/m 和 0.99。核心验证站点表明，与原始 SMAP 土壤湿度数据（0.04 m/m 0.04 m/m）相比，重建的土壤湿度数据具有一致的 ubRMSD。 PhyFill 方法可以生成全球连续、高精度的土壤湿度估计值，为先进的水文应用、全球土壤湿度干燥事件和模式提供卓越的支持。