SDFNet: Snow Depth Retrieval Based on a Multi-Scale Feature Fusion Network for the Qinghai-Tibet Plateau

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.