Bridging temporal and spatial gaps in passive microwave observations for global snow depth estimation

Establishing a long-term, consistent snow depth record from multiple passive microwave (PMW) sensors is critical for hydrological modeling and climate change research. However, integrating data from different PMW sources is challenging due to variations in sensor design and observation configurations. This study proposes a novel cross-sensor calibration and fusion framework that harmonizes brightness temperatures (TBs) from the Microwave Radiation Imager (MWRI) onboard the Fengyun-3 (FY-3) satellites with AMSR-E/AMSR2 data. Using AMSR2 as a reference, a Simultaneous Conical Overpass (SCO) calibration method is applied to align FY-3B and FY-3D MWRI data. The framework incorporates orbital differences to synergistically fuse cross-calibrated TBs, enhancing temporal continuity and filling data gaps. A spatiotemporal sample adaptation strategy is introduced to match TB observations from cross-calibrated orbital PMW products with hourly in situ snow depth measurements, generating a reliable training dataset for Random Forest (RF) model development. The trained model is subsequently applied to a consistent multi-sensor gridded TB dataset to produce a global snow depth record from 2002 to 2023. Validation results show excellent inter-sensor consistency, with R² values exceeding 0.99 across all TB channels. The incorporation of FY-3 data significantly improves spatial coverage: after 2012, the annual land coverage of ascending-orbit TBs increased from 74.80% to 88.56%, and descending-orbit TBs from 73.93% to 87.94%. The resulting snow depth product demonstrates high accuracy, broad spatial coverage, and strong temporal continuity. This study offers a scalable framework for fusing heterogeneous PMW data, providing a high-quality, globally consistent snow depth product for climate and hydrological studies.