Integrating Satellite and Field Data for Glacier Melt Modeling in High-Mountain Asia: A Case Study on Passu Glacier

Highlights: What are the main findings? A simplified melt model accurately estimated bare ice melt on Passu Glacier (error = 0.48 m w.e., 9%). Satellite-derived albedo variability significantly influenced melt estimates. Elevation was the dominant topographic factor controlling ice melt, exceeding the influence of slope or aspect. What is the implication of the main finding? The melt model is reliable and scalable for estimating bare ice melt in remote, data-scarce regions. Using multi-date satellite imagery for albedo improves the accuracy of ice melt simulations. The approach supports effective glacier monitoring in the Karakoram with minimal field data. Glaciers in High-Mountain Asia, the so-called “Third Pole,” are critical water sources but remain poorly monitored due to rugged topography and limited accessibility. We present an integrated approach that combines remote sensing with ground-based observations to model ice melt of the Passu Glacier (Pakistan) from 5 August to 13 October 2023. Meteorological data from two automatic weather stations and ablation measurements from four stakes were used together with satellite-derived albedo (Landsat 8 OLI), surface temperature (Landsat 9 TIRS), and topography (ALOS AW3D30 DSM) to implement an enhanced T-index melt model accounting for net shortwave and longwave radiation. Model performance was evaluated against station and satellite data and ablation stake measurements using leave-one-out cross-validation. The estimated total ice melt volume was 16 million m3 w.e. during the monitoring period, with an average melt of 3.60 m w.e. The model reproduced observed stake ablation with an uncertainty of 0.48 m w.e. (9% of average measured melt). Elevation was identified as the dominant melt driver (β = −0.501, unique R2 = 0.199), with aspect and slope exerting secondary influences through their effect on solar radiation and shading. Our findings demonstrate that combining minimal but strategically located field data with satellite products provides a physically consistent and scalable framework for glacier melt estimation in data-scarce regions of the Third Pole, with relevance for hydrological monitoring and climate adaptation.