Physics-Constrained Deep Learning Models for Microwave Retrieval of High-Resolution Soil Moisture

Soil moisture is an essential climate variable that directly influences many hydrological, agricultural, and water-cycle processes. Many satellites have been launched and are still being launched to map soil moisture accurately at a global scale. Motivated by the coarse resolution of existing satellite products, many statistical, physics-based, and machine learning-based methods have been proposed to downscale soil moisture to much finer spatial scales. In this paper, we propose a novel deep learning approach that is constrained by the Tau-omega radiative transfer model to enhance the resolution of surface soil moisture. We demonstrate the proposed framework by downscaling Soil Moisture Active Passive (SMAP) L-band brightness temperature (TB) with C-band synthetic aperture radar (SAR) backscattering coefficient (σ0) imagery from Sentinel-1A/B and subsequently retrieving high spatial resolution (1km) soil moisture.@INPROCEEDINGS{10641600, author={Kannan, Archana and Tsagkatakis, Grigorios and Moghaddam, Mahta}, booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, title={Physics-Constrained Deep Learning Models for Microwave Retrieval of High-Resolution Soil Moisture}, year={2024}, volume={}, number={}, pages={1478-1480}, keywords={Deep learning;Satellites;Soil moisture;Neural networks;Surface soil;Sentinel-1;Moisture;Physics-constrained deep learning;Tauomega model;Soil moisture;Downscaling}, doi={10.1109/IGARSS53475.2024.10641600}}