Machine Learning-Based Downscaling of Glacier Surface Temperature to 10-m Resolution Using NDSI-Driven Emissivity Correction

Accurate high-resolution glacier surface temperature (GST) mapping is critical for monitoring Himalayan glacier dynamics. This study introduces a method to predict GST at 10-m resolution by integrating Sentinel-2, Landsat thermal infrared sensor (TIRS), and shuttle radar topography mission (SRTM) digital elevation model (DEM) data in Google Earth engine (GEE). A dynamic emissivity correction, using normalized difference snow index (NDSI) thresholds (clean ice: NDSI $\geq 0.4$ , $\varepsilon =0.99$ ; debris-covered ice: $\varepsilon =0.96$ ), addresses a critical limitation in existing GST retrievals. A random forest (RF) regression model trained on stratified elevation classes achieved an ${R} ^{2}$ of 0.91 and mean absolute error (MAE) of $2.53~^{\circ }$ C, downscaling Landsat’s 30-m thermal data. Results reveal fine-scale GST variability driven by debris cover and topography, demonstrating the method’s utility for glacier studies.