Elevation-Dependent Glacier Albedo Modelling Using Machine Learning and a Multi-Algorithm Satellite Approach in Svalbard

Glacier surface albedo controls solar energy absorption and Arctic mass balance, yet comprehensive modelling approaches remain limited. This study develops and validates multiple modelling frameworks for glacier albedo prediction using automatic weather station (AWS) data from Hansbreen and Werenskioldbreen in southern Svalbard during the 2011 ablation season. We compared three point-based approaches across elevation zones. At lower elevations (190 m), linear regression models emphasising snowfall probability or temperature controls achieved excellent performance (R2 = 0.84–0.86), with snowfall probability contributing 65% and daily positive temperature contributing 86.3% feature importance. At higher elevations (420 m) where snow persists, neural networks proved superior (R2 = 0.65), with positive degree days (72.5% importance) driving albedo evolution in snow-dominated environments. Spatial modelling extended point predictions across glacier surfaces using elevation-dependent probability calculations. Validation with Landsat 7 imagery and multi-algorithm comparison (n = 5) revealed that while absolute albedo values varied by 12% (0.54–0.60), temporal dynamics showed remarkable consistency (27.8–35.2% seasonal decline). Point-to-pixel validation achieved excellent agreement (mean absolute difference = 0.03 ± 0.02, 5.3% relative error). Spatial validation across 173,133 pixel comparisons demonstrated good agreement (r = 0.62, R2 = 0.40, RMSE = 0.15), with an accuracy of reproducing temporal evolution within 0.001–0.021 error. These findings demonstrate that optimal glacier albedo modelling requires elevation-dependent approaches combining physically based interpolation with machine learning, and that temporal pattern reproduction is more reliably validated than absolute values. The frameworks provide tools for understanding albedo-climate feedback and improving mass balance projections in response to Arctic warming.