A Microwave–Optical Multi-Stage Synergistic Daily 30 m Soil Moisture Downscaling Framework

Accurate daily surface soil moisture (SSM) mapping at high spatial resolution (e.g., 30 m) remains challenging due to individual satellite sensor limitations. Although passive microwave sensors provide frequent coarse-resolution observations and synthetic aperture radar (SAR) offers high-resolution data intermittently, achieving both simultaneously requires sensor synergy. This paper introduces the microwave–optical multi-stage synergistic downscaling framework (MMSDF) to generate daily 30 m SSM products. The framework integrates SMAP L4 (9 km), MODIS data (500 m–1 km), harmonized Landsat Sentinel-2 (HLS, 30 m), radiometric terrain corrected Sentinel-1 (RTC-S1, 30 m), and auxiliary geographic data. It comprises three stages: (1) downscaling SMAP L4 to 1 km via random forest; (2) calibrating Sentinel-1 water cloud model (WCM) using intermediate 1 km SSM to retrieve 30 m SSM without in situ calibration; and (3) fusing daily 1 km SSM and intermittent 30 m WCM-derived retrievals using the spatial–temporal fusion model (ESTARFM) to generate seamless daily 30 m SSM maps. Validation against in situ measurements from 16 sites in Hunan Province, China (summer 2024) yielded R of 0.54 and RMSE of 0.045 cm3/cm3. Results demonstrate the framework’s capability to synergize multi-source data for high-resolution daily SSM estimates valuable for hydrological and agricultural applications.