Deep learning applied to Sentinel-1 data shows doubling of glacier area loss in Svalbard compared to 1980–2010

Glaciers in Svalbard are undergoing rapid changes due to Arctic Amplification that demand frequent and accurate monitoring. We present a deep learning model, Intensity-Coherence-Evolution-mapper (ICEmapper) designed to extract annual glacier outlines from Sentinel-1 time series data regardless of cloud cover. The model combines SAR backscatter intensity and interferometric coherence. In extensive validation tests against manually digitised optical imagery, ICEmapper demonstrates human-expert accuracy, with intersection of union score higher than 0.95, total area discrepancies below 0.5%, median distance deviations under 15 m, and 95th percentile deviations within 250 m. Additionally, we report calibrated uncertainties of our classification results at the pixel level, allowing for detailed analysis of significant changes as well as total area uncertainty estimation. This performance allowed us to construct a continuous inventory of glacier outlines in Svalbard from 2016 to 2023.

The results of area change analysis suggest a substantial escalation in the rate of glacier area loss to about 180 km² a⁻¹ in the last decade, nearly doubling from the previously reported ~80 km² a⁻¹ (1980–2010; Nuth et al., 2013). This increase is predominantly driven by enhanced calving at tidewater glaciers, although climatic signal also shows a significant correlation with the area loss of land-terminating glaciers. Surging glaciers, particularly in the Nathorstbreen system and Austfonna, exhibited unique behaviours that can temporarily increase the total glacier area. In 2016, the Nathorstbreen system gained 107.76 km², while Austfonna, Basin-3 expanded by 86.54 km² as compared to the Randolph Glacier Inventory, jointly offsetting net losses by approximately two years. In the last decade, however, surging glaciers lost area more rapidly (−0.57 km² a⁻¹ on average) than the non-surging ones (−0.09 km² a⁻¹). Additionally, our analysis uncovered a surge in Austfonna, Basin-7 starting in 2019 and not reported previously, emphasising the capability of annually updated inventories to complement other methods of surge detection.

Our methods have the potential to be transferred to other glacierised regions and enhance monitoring and understanding of glacier area changes on larger scales using Sentinel-1 data.