Surface soil moisture plays a pivotal role in various hydrological processes. Precisely assessing soil moisture with high resolution is crucial for effective water resource management, informed agricultural decision-making, and in-depth climate change research. While passive microwave remote sensing is a primary technology for regional soil moisture monitoring, its practical application is hindered by data discontinuity and low resolution. To address these challenges, we propose an integrated learning framework to enhance the continuity and resolution of soil moisture data across China. Leveraging low-resolution passive microwave soil moisture data, moderate-resolution assimilated soil moisture data, and multiple high-resolution ancillary inputs, the network effectively captures the spatiotemporal dynamics of soil moisture through integrating gap-filling, multisource fusion, and spatial downscaling processes. Validation against in situ data demonstrates the significant enhancements achieved by the proposed method, with an average R value of 0.706 and an average unbiased root mean square error of 0.055 m3/m3. Comparative analysis further confirms its superior accuracy and robustness across diverse regions. These findings highlight the potential of this integrated learning framework to advance hydrological applications, enhance agricultural production, and support climate research.