An Approach for Validation of Soil Moisture Algorithms in Heterogenous Agricultural Regions Using Synthetic Dataset

An accurate estimation of soil moisture (SM) is crucial for management of water resources, drought assessment, hydrology, and crop yield. The dynamics of near-surface SM can be effectively captured using L-band active and passive (AP) microwave (MW) observations. While current global SM information is available through passive L-band missions like NASA's Soil Moisture Active Passive (SMAP) and ESA's Soil Moisture and Ocean Salinity (SMOS), their spatial resolutions of 36 km and 25 km, respectively, are too coarse to provide precise SM estimates in heterogeneous agricultural regions. To address this limitation, this study focuses on developing a synthetic dataset to validate SM scaling algorithms and explore the synergies between AP MW observations at different spatial scales. By employing this synthetic AP MW dataset, the effectiveness of soil moisture algorithms in agricultural regions will be assessed.