A novel approach for Quasi-Global daily continuous surface soil moisture downscaling at 500-m resolution using CYGNSS observations

Abstract

Accurate Surface Soil Moisture (SSM) with high spatiotemporal resolution is essential for hydrological modeling and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) provides quasi-global SSM with relatively high temporal resolution, while its coarse spatial resolution limits regional usability. This study proposed a novel approach to generate daily continuous quasi-global SSM at 500-m resolution by integrating CYGNSS observations with optical imagery, without relying on in-situ SSM inputs. The method includes: (1) reconstruction of daily Surface Reflectivity (SR) using the Previously-Observed Behavior Interpolation (POBI) algorithm and retrieval of 9-km SSM via a modified Reflectivity-Vegetation-Roughness (R-V-R) model; (2) constructing a direct relationship between SSM and optical reflectance using the enhanced OPtical TRApezoid Model (OPTRAM) as the foundational basis for subsequent downscaling process; (3) downscale coarse-resolution CYGNSS SSM using the Bayesian algorithm without requiring any in-situ SSM observations as inputs. The framework is applied over August 2019–2022 and validated against in-situ SSM from 194 International Soil Moisture Network (ISMN) sites across diverse climate and land cover conditions. Results show improved spatial detail while maintaining accuracy, with a mean unbiased root-mean-square error (ubRMSE) of 0.061 cm3/cm3 and a positive GDOWN value of 0.017, indicating effective SSM downscaling. In addition, the proposed method outperforms both Linear and Random Forest models while maintaining robust performance. Overall, it offers a scalable solution for generating high-resolution, daily SSM products directly from satellite data.