SOIL MOISTURE ESTIMATION FROM POLARIMETRIC SAR USING A PHYSICS AWARE AI MODEL

This study explores the synergy of electromagnetic data modeling and Artificial Intelligence (AI) algorithms for soil moisture retrieval over agricultural fields using Polarimetric SAR (PolSAR) data. SAR acquisitions are considered as a valuable source for accurate estimation of soil moisture in agricultural areas. However, its retrievals are influenced by various factors, including vegetation cover. In this context, the polarimetric information of SAR acquisitions allows the interpretation of the occurring scattering processes. The proposed approach involves a physics-aware AI algorithm based on two Artificial Neural Networks (ANNs), trained on electromagnetic (EM) model simulations at L-band. Starting from a full polarimetric Covariance Matrix, the first AI model separates the different scattering contributions, estimating surface and double-bounce scattering mechanisms while minimizing the attenuation effects caused by the vegetation layer. Then, the estimated surface and double-bounce components are fed to an additional AI model to retrieve soil moisture. Field campaign data over actual corn fields were considered and ingested by the EM model to generate synthetic SAOCOM-like case studies which were used to validate the approach within the simulated domain.