Glacier Boundary Extraction over the Tibetan Plateau Using a Double Random Forest Model with Multi-Temporal Sentinel-1/2 Data

Glacier boundary extraction on the Tibetan Plateau (TP) faces persistent challenges due to rugged terrain, seasonal snow, extensive debris cover, and topographic shadows. Traditional methods utilizing single-source or single-temporal data often yield limited accuracy. Thus, we propose an automated Double Random Forest (Double-RF) framework integrating single- and multi-temporal features from Sentinel-1 (SAR) and Sentinel-2 (Optical) data within the Google Earth Engine. We established a multidimensional feature space comprising spectral, textural, polarimetric, and topographic attributes. Feature optimization was performed using importance metrics and out-of-bag (OOB) error. A hierarchical classification strategy was employed: the first RF identifies clean glaciers and glaciers in shadow, while the second RF executes refined boundary extraction of debris-covered glaciers to mitigate spectral confusion. The results indicate that the Double-RF method significantly achieves an overall accuracy exceeding 0.84 across all sub-basins and reaching above 0.95 at best. The derived glacier inventory reveals a distinct spatial pattern: higher concentrations in the western and peripheral regions compared to the eastern and interior TP. Glaciers are predominantly distributed on shaded aspects with gentle-to-moderate slopes, highlighting the combined influence of climatic gradients and topographic controls. This multi-source, multi-temporal fusion strategy provides a robust methodological foundation for long-term glacier monitoring over the TP.