The Qinghai–Tibet Plateau (QTP), one of China’s most snow-rich regions, has an extremely fragile ecosystem, with drought being the primary driver of ecological degradation. Given that the water resources in this region predominantly exist in the form of snow, high-spatiotemporal-resolution snow mapping is essential for understanding snow distribution and managing snow water resources effectively. However, although FY-4A/AGRI is capable of obtaining wide-area remote sensing data, only the first to third bands have a resolution of 1 km, which greatly limits its ability to produce high-resolution snow maps. This study proposes U-SwinFusionNet (USFNet), a deep learning-based snow cover retrieval algorithm that leverages the multi-scale advantages of FY-4A/AGRI remote sensing data in the shortwave infrared and visible bands. By integrating 1 km and 2 km resolution remote sensing imagery with auxiliary terrain information, USFNet effectively enhances snow cover mapping accuracy. The proposed model innovatively combines Swin Transformer and convolutional neural networks (CNNs) to capture both global contextual information and local spatial details. Additionally, an Attention Feature Fusion Module (AFFM) is introduced to align and integrate features from different modalities through an efficient attention mechanism, while the Feature Complementation Module (FCM) facilitates interactions between the encoded and decoded features. As a result, USFNet produces snow cover maps with a spatial resolution of 1 km. Experimental comparisons with Artificial Neural Networks (ANNs), Random Forest (RF), U-Net, and ResNet-FSC demonstrate that USFNet exhibits superior robustness, enhanced snow cover continuity, and lower error rates. The model achieves a correlation coefficient of 0.9126 and an R² of 0.7072. Compared to the MOD10A1 snow product, USFNet demonstrates an improved sensitivity to fragmented and low-snow-cover areas while ensuring more natural snow boundary transitions.

High-resolution snow depth (SD) information supports hydrological and cryospheric studies in complex mountain regions. Coarse SD products limit representation of terrain-related snow variability in these environments. This study proposes SDFNet, a Snow Depth Fusion Network combining Sentinel-1 SAR, Landsat optical data, digital elevation model (DEM) variables, and fractional snow cover (FSC). Multi-scale feature extraction, channel enhancement, and feature aggregation modules represent relationships among these inputs. Observations from 95 meteorological stations across the Qinghai-Tibet Plateau supplied supervision and reference measurements for evaluation. Station SD was the regression target rather than a prediction input. In the station-based comparison, SDFNet achieved an RMSE of 5.246 cm and an R² of 0.702. It had the lowest RMSE and highest R² among the compared models. However, SD above 40 cm produced an RMSE of 21.927 cm and a bias of −16.061 cm, indicating substantial deep-snow underestimation. Sparse station coverage limits inference beyond the sampled environments, and independent spatial validation remains necessary.