A Soil Moisture Downscaling Residual Dense Network Considering Spatiotemporal Relationship

Soil moisture (SM) is a key state variable in hydrology, climatology and water resource management. Microwave remote sensing can retrieve soil moisture at regional or global scales, but limited by spatial resolution, it is difficult to accurately reflect the details of soil moisture. To overcome this limitation, in this paper, we propose a Soil Moisture Downscaling residual dense Network (SMDN) based on spatiotemporal information. We model the relationship between low-resolution geoscience parameters and soil moisture, and then downscale soil moisture products by applying the model to high-resolution geoscience parameters. On this basis, the residual correction is performed on the soil moisture downscaling results to generate high-precision soil moisture products. Visual evaluation and quantitative experiments show that the proposed network can effectively improve the spatial detail information, maintain the spatial distribution pattern, and have strong stability.