Field-scale soil moisture estimated from Sentinel-1 SAR data using a knowledge-guided deep learning approach

• URL: http://arxiv.org/abs/2505.00265v1
• Date: Thu, 01 May 2025 03:12:25 GMT
• Title: Field-scale soil moisture estimated from Sentinel-1 SAR data using a knowledge-guided deep learning approach
• Authors: Yi Yu, Patrick Filippi, Thomas F. A. Bishop,
• Abstract summary: This research presents preliminary efforts for developing a knowledge-guided deep learning approach.<n>It integrates the water cloud model (WCM) principles into a long short-term memory (LSTM) model.<n>Our proposed approach builds upon the soil backscatter coefficients isolated from the total backscatter, together with Landsat-resolution vegetation information and surface characteristics.<n>Results showed the proposed approach reduced SM retrieval 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.
• Score: 6.595840767689357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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