Field-scale soil moisture estimated from Sentinel-1 SAR data using a knowledge-guided deep learning approach
This addresses soil moisture monitoring for agricultural and environmental applications, but it is incremental as it builds on existing models with minor improvements.
The research tackled soil moisture estimation from Sentinel-1 SAR data by integrating water cloud model principles into an LSTM model, reducing retrieval uncertainties by 0.02 m³/m³ and achieving correlation coefficients up to 0.64 in diverse agricultural areas.
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.