Surface soil moisture (SM) plays an important role in water and energy cycles. Passive microwave remote sensing observation has become the main means of obtaining large-scale surface SM. Due to its low spatial resolution, the spatial downscaling is required. With the development of artificial intelligence, data-driven SM downscaling models have emerged in recent years and have shown better accuracy than traditional physical models. However, data-driven SM downscaling models still have problems such as poor interpretability and easy overfitting. Therefore, this paper proposes a new SM downscaling model based on physical rule-constrained deep learning, named Physics-informed Soil Moisture Downscaling Deep Neural Network (PhySoilNet). This model adds the physical relationship between SM and the downscaling factor Land surface Evaporative Efficiency, as well as the saturated and residual boundary of SM into the Loss function of deep learning, thereby constraining the neural network. Results showed that PhySoilNet successfully downscaled the 9 km Soil Moisture Active Passive (SMAP) SM to 500 m, and performed well in the evaluations with in-situ, aerial, and SMAP SM. Compared to the downscaling model of only data-driven, the PhySoilNet had better performance in all evaluations, and the metrics in the in-situ SM network evaluation were improved by 20 % for R, 9.9 % for ubRMSE, 7.2 % for MAE, and 7.2 % for RMSE. At the same time, the number of SM predicted by PhySoilNet that outside the reasonable SM boundary range was significantly reduced. This fully demonstrates that data-driven based on physical rule constraints can achieve SM downscaling more effectively. Coupling physical rules and deep learning can fully utilize the powerful fitting ability of data-driven methods while improving the generalization ability and interpretability of downscaling models through prior physical knowledge.

中文翻译：

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PhySoilNet：具有物理规则约束的微波卫星土壤水分深度学习降尺度模型


表层土壤水分 （SM） 在水和能量循环中起着重要作用。被动微波遥感观测已成为获得大尺度表面 SM 的主要手段。由于其空间分辨率低，需要空间降尺度。随着人工智能的发展，近年来出现了数据驱动的 SM 缩小模型，并显示出比传统物理模型更好的准确性。然而，数据驱动的 SM 降尺度模型仍然存在可解释性差、易过拟合等问题。因此，本文提出了一种新的基于物理规则约束深度学习的 SM 降尺度模型，命名为 Physics-informed Soil Moisture Downscaling Deep Neural Network （PhySoilNet）。该模型将 SM 与降尺度因子 Land surface Evaporative Efficiency 之间的物理关系，以及 SM 的饱和边界和残差边界加入深度学习的损失函数中，从而约束神经网络。结果表明，PhySoilNet 成功地将 9 km 土壤水分主动被动 （SMAP） SM 缩小到 500 m，并在原位、航空和 SMAP SM 评估中表现良好。与仅数据驱动的缩小模型相比，PhySoilNet 在所有评估中都有更好的性能，原位 SM 网络评估中的指标对 R 提高了 20 %。 ubRMSE 为 9.9 %，MAE 为 7.2 %，RMSE 为 7.2 %。同时，PhySoilNet 预测的超出合理 SM 边界范围的 SM 数量显著减少。这充分证明了基于物理规则约束的数据驱动可以更有效地实现 SM 降容。 将物理规则与深度学习耦合可以充分利用数据驱动方法的强大拟合能力，同时通过先验的物理知识提高缩小模型的泛化能力和可解释性。