Abstract: The surface albedo in seasonal snow-covered areas is characterized by rapid changes and significant spatiotemporal heterogeneity. Hence, surface albedo data with a high spatiotemporal resolution are required to monitor the freeze-thaw process of snow. However, constrained by sensor performance and inversion methods, existing surface albedo data exhibit a low spatiotemporal resolution, failing to meet the demand for the effective monitoring of spatiotemporal variations in seasonal snow albedo. To address this challenge, this study developed a method for generating high-resolution surface albedo data of seasonal snow-covered areas. Specifically, spatiotemporal fusion was conducted for surface albedo data estimated based on data from the Huanjing-1 A/B (HJ-1 A/B) minisatellites for environmental protection and disaster monitoring, as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) satellites. Consequently, surface albedo data with high spatiotemporal resolutions of 1 d and 30 m were generated. The results indicate that the proposed method exhibited enhanced accuracy for snow-covered periods, significantly improving the spatiotemporal resolution of surface albedo data. This method can accurately capture the rapid changes and spatiotemporal heterogeneity of surface albedo in seasonal snow freeze-thaw processes, providing high-quality fundamental data for investigating regional hydrology and water resources.