Integrating Geophysical and Remote Sensing Techniques for Mountain Permafrost mapping in the Northwestern Himalaya

Permafrost mapping in high-mountain environments is essential for understanding cryospheric processes and assessing cryopheric hazards due to climate change. Remote sensing-based permafrost mapping commonly relies on multiple climatological and topographical parameters, including land surface temperature, snow index, soil moisture, elevation, slope and aspect. However, accurately predicting subsurface permafrost conditions remains challenging.  For mountain permafrost studies, rock glaciers are widely used as proxies for permafrost presence, and machine-learning models are often trained primarily on their spatial distribution. This approach introduces a systematic bias, as rock glaciers are typically concentrated above ~4200 m a.s.l., whereas permafrost occurrence is also controlled by local thermal regimes, soil properties, and moisture conditions, allowing its presence within valley settings. In this study, we investigate mountain permafrost occurrence and active layer characteristics in contrasting high-altitude environments using an integrated remote sensing and geophysical approach. Ground Penetrating Radar (GPR) surveys were carried out at two different geographical locations, Matiyan, Jammu and Kashmir, and Baralachala, Himachal Pradesh. The locations were chosen for their diverse lithology, elevation and climatic conditions. GPR data was collected in both common-offset and multi-offset modes using 80 MHz and 450 MHz antennas, aiming to enhance depth and resolution. The GPR data reveal clear subsurface signatures of frozen ground, including ice-rich layers and distinct active layer thicknesses, even in areas lacking visible rock glacier morphology. The ALT measurements ranged from 0.6 m to 1.2 meters depth in Matiyan, while Baralachala showing a shallower active layer of 0.2 m to 0.5 meters and reflecting colder climatic conditions and less surface disturbance. The findings of this study highlight the limitations of proxy-based permafrost mapping and provide a deeper understanding of mountain permafrost in changing climatic scenarios. Thus, the integration of remote sensing data with subsurface observations provides improved understanding of thermo-hydrological controls on mountain permafrost distribution.