Snow Depth Estimation with Combined Terrain and Remote Sensing Information over High-Latitude Asia

High-resolution snow depth monitoring is a crucial foundation for precise disaster early warning and optimal water resource management. Traditional snow depth estimation methods mainly rely on passive microwave remote sensing data, but due to their low spatial resolution, they have difficulties capturing the subtle changes in snow depth in complex terrain. Existing deep learning methods mostly adopt single-modal or simple band fusion, failing to fully utilize the complementarity among multi-source data and not considering that terrain factors can lead to misjudgment of the true snow signal. Therefore, this paper proposes a dual-branch intermediate fusion network (TACMF-Net) for high-latitude regions in Asia. By introducing terrain factors (DEM, slope, aspect) and conducting cross-modal feature interaction, it achieves efficient collaboration of multi-source remote sensing data. Research shows that our method has extremely high accuracy and robustness on the self-made multi-source snow depth terrain dataset.