Glacier mapping from Sentinel-1 SAR time series with deep learning in Svalbard

Glaciers are one of the essential climate variables. Tracking their areal changes over time is of high importance for monitoring the impacts of climate change and designing adaptation strategies. Mapping glaciers from optical remote sensing data might result in a very limited temporal resolution due to the absence of cloud-free imagery at the end of the ablation season. Synthetic aperture radar (SAR) solves this problem as it can operate in almost all weather conditions. Here, we present a deep learning strategy for glacier mapping based solely on Sentinel-1 SAR data in Svalbard. We test two options for integrating SAR image time series into deep learning models, namely, 3D convolutions and long short-term memory (LSTM) cells. Both proposed models achieve an intersection over union (IoU) of 0.964 on the test subset. Our results highlight the applicability of SAR data in glacier mapping with the potential to obtain glacier inventories with higher temporal resolution. We shared our dataset, code-base and pretrained models at https://github.com/konstantin-a-maslov/icemapper.