用雷达数据估土壤湿度，结合物理模型提升精度。
Field-scale soil moisture estimated from Sentinel-1 SAR data using a knowledge-guided deep learning approach
- 将水云模型原理融入LSTM网络，兼顾物理合理性与时序建模。
- 误差降低0.02 m³/m³，相关系数最高达0.64，跨植被区表现稳定。
- 适合遥感、农业监测领域研究者参考，尤其关注地表参数反演。
基于主动微波数据的土壤湿度（SM）反演仍面临雷达后向散射与地表特征复杂交互的挑战。尽管水云模型（WCM）提供了半物理解释框架，但其经验成分常导致在多样农业景观中性能受限。本文提出一种知识引导的深度学习方法，将WCM原理嵌入长短期记忆（LSTM）模型，利用哨兵1号合成孔径雷达（Sentinel-1 SAR）数据估算田块尺度土壤湿度。该方法基于从总后向散射中分离出的土壤后向散射系数，结合陆地卫星分辨率植被信息及地表特征。通过四折空间交叉验证，对比原位土壤湿度数据评估模型性能。结果表明，该方法将土壤湿度反演不确定性降低0.02 m³/m³，相关系数（R）最高达0.64，在不同植被覆盖与地表条件下均表现出良好稳定性，证实了其对水云模型过度简化的有效缓解潜力。
原文摘要 · Abstract (English)
Soil moisture (SM) estimation from active microwave data remains challenging due to the complex interactions between radar backscatter and surface characteristics. While the water cloud model (WCM) provides a semi-physical approach for understanding these interactions, its empirical component often limits performance across diverse agricultural landscapes. This research presents preliminary efforts for developing a knowledge-guided deep learning approach, which integrates WCM principles into a long short-term memory (LSTM) model, to estimate field SM using Sentinel-1 Synthetic Aperture Radar (SAR) data. Our proposed approach leverages LSTM's capacity to capture spatiotemporal dependencies while maintaining physical consistency through a modified dual-component loss function, including a WCM-based semi-physical component and a boundary condition regularisation. The proposed approach is built upon the soil backscatter coefficients isolated from the total backscatter, together with Landsat-resolution vegetation information and surface characteristics. A four-fold spatial cross-validation was performed against in-situ SM data to assess the model performance. Results showed the proposed approach reduced SM retrieval uncertainties by 0.02 m$^3$/m$^3$ and achieved correlation coefficients (R) of up to 0.64 in areas with varying vegetation cover and surface conditions, demonstrating the potential to address the over-simplification in WCM.
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