Mapping snow cover frequency at 30 m for studying seasonal variations and topographic controls on the Tibetan Plateau - Wang, Guigang; Che, Tao; Dai, Liyun; Hu, Yanxing; Wu, Jun; Meng, Saiyao; Kong, Chuilei; Wang, Jing; Feng, Dongdong; Wang, Shijie; Li, Xuemei (Journal of hydrology (Amsterdam), 2025)
•An innovative 30 m resolution snow cover frequency map of the Tibetan Plateau was developed using over 500,000 Landsat and Sentinel-2 images.•A specialized snow mapping algorithm was designed to improve detection in shaded and low-illumination areas.•The SCOF maps accurately characterize the spatial heterogeneity of mountain snow at the hillslope scale.•A 30 m resolution analysis of seasonal variations and topographic controls on snow cover across different elevation zones provides new insights into snow distribution on the Tibetan Plateau. Estimating snow parameters (e.g., snow cover, snow depth) at hillslope scales (<100 m) is an urgent but highly challenging research task. Remote sensing has become an indispensable tool for monitoring large-scale snow cover. However, existing studies on spatial patterns of snow cover typically focus on scales of 500–5000 m due to the trade-offs between temporal and spatial resolution in remote sensing sensors. This limitation hinders accurately representing the high spatial heterogeneity in mountain snow. To this end, we used a straightforward metric and developed an innovative 30 m average monthly Snow Cover Frequency (SCOF) map for the Tibetan Plateau (TP) utilizing high-resolution images from Landsat and Sentinel-2 satellites, spanning from 2000 to 2024. These maps provide a novel perspective for analyzing seasonal variations in snow cover and its relationship with topography. First, we designed a specific snow mapping algorithm tailored to shaded and low-illumination areas, which were depicted through terrain modeling. Validation against in situ observations and very-high-resolution remote sensing data demonstrated that snow could be effectively extracted even in challenging shadowed conditions. Next, SCOF maps were generated using snow cover data extracted from over 500,000 Landsat and Sentinel-2 images. Comparative analysis demonstrated that SCOF maps accurately characterize the spatial heterogeneity of mountain snow, with strong correlations with in situ observations, providing significantly enhanced spatial details compared to MODIS-derived SCOF maps. Finally, the spatial patterns of SCOF, along with seasonal variations and their relationship with topography, were meticulously documented. Key findings include: (1) SCOF exhibits apparent seasonal variations at elevations between 1500 and 6300 m, with the highest value typically observed in February and the lowest in August. However, above 7100 m, the highest SCOF occurs in July and the lowest in February, presenting an almost opposite seasonal pattern. Notably, although SCOF remains high at elevations of 6300–7100 m, seasonal variation are minimal, with the snow remaining relatively balanced across the seasons. (2) Generally, SCOF increases with elevation below 6300 m, showing a gradual rise below 5000 m but becoming significantly more rapid above this elevation. Above 7100 m, an intriguing phenomenon is observed: SCOF is unexpectedly lower in winter than in summer. In this region, SCOF in the warm season remains constant with increasing elevation, with slight increases in individual months, whereas in the cold season, SCOF shows a decreasing trend. (3) Elevation, distance to the coastline, and the topographic relief index are found to be important factors influencing snow distribution on the TP. The high-resolution SCOF maps presented in this study enhance our understanding of hillslope-level snow cover patterns in alpine regions lacking in situ observations, contributing to improved research on snow hydrology.