Spatiotemporal Reconstruction of FY-3B Soil Moisture Using a Hybrid Attention and Partial Convolution Neural Network

China’s FY-3/4 satellite constellation has significantly advanced global environmental monitoring capabilities. However, the inherent temporal resolution limitations of polar-orbiting satellites result in discontinuous spatiotemporal coverage of current soil moisture products, thereby constraining their application in global hydrological modeling. To overcome this challenge, this study introduces a dedicated spatiotemporal reconstruction model that enhances the spatial continuity of FY-3B satellite soil moisture datasets. The proposed model utilizes a dual-channel input architecture integrating continuous observation data with dynamic mask matrix and leverages partial convolution for effective collaborative spatiotemporal feature extraction. Contextual attention and multilayer transformer encoder are incorporated to generate seamless global daily soil moisture product (2010–2019). Validation indicate notable improvements in accuracy: 1) in situ validation increased the mean correlation coefficient from 0.542 to 0.671 and reduce the root mean square error from 0.147 to 0.143 m³/m³; 2) temporal consistency analysis confirms that the reconstructed sequence remains highly synchronized with the original soil moisture products; 3) simulated missing region experiments yielded a coorelation of 0.953 with the original products, with a bias as low as –0.004 m³/m³ and an unbiased root mean square error of 0.006 m³/m³. Compared to traditional partial convolution methods, this approach enhances global accuracy by increasing the correlation by 23.8%. Notably, this research marks the first implementation of a hybrid attention-partial convolution deep learning model to generate a seamless global daily soil moisture product derived from Fengyun satellites, effectively addressing previous reconstruction limitations.