Soil Moisture Retrieval Using in Situ and Data Simulation Regularized Deep Learning Models

Retrieval of Surface Soil Moisture (SSM) over large scales at high spatial resolution is crucial for numerous applications. Existing solutions rely on the analysis of remote sensing platforms or in situ measurements that are either too coarse in their resolution or too localized to address the aforementioned need. In this work, we propose a novel deep learning approach for reliably estimating SSM at a high spatial resolution of 1 km over broad regions. To achieve this objective, the proposed framework employs a Convolutional Neural Network that can capture both multi-modal and spatial correlations. Introducing a novel loss function, the proposed scheme can leverage limited in situ observations while also generating estimates consistent with physical models. This is achieved through the utilization of coarse-resolution data assimilation estimates. For training and assessing the performance of the proposed framework, a novel dataset is generated by combining information from remote sensing, in situ measurements, and data assimilation estimates. Experimental analysis demonstrates that the proposed approach can provide accurate retrieval of SSM, significantly outperforming existing products.