Soil Moisture Retrival Using in Sity 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 plat- forms 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 ap- proach for reliably estimating SSM at a high spatial reso- lution of 1 km over broad regions. To achieve this objec- tive, the proposed framework employs a Convolutional Neu- ral 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 perfor- mance of the proposed framework, a novel dataset is gener- ated 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 exist- ing products.