Calving front positions for 42 key glaciers of the Antarctic Peninsula: a sub-seasonal record from 2013 to 2023 based on a deep learning application to Landsat multispectral imagery

Kurzfassung

Calving front positions of marine-terminating glaciers are an essential parameter for understanding dynamic glacier changes and constraining ice modelling. In particular, for the Antarctic Peninsula, where the current ice mass loss is driven by dynamic glacier changes, accurate and comprehensive data products are of major importance. Current calving front data products are limited in coverage and temporal resolution because they rely on manual delineation, which is time-consuming and unfeasible for the increasing amount of satellite data. To simplify the mapping of calving fronts we apply a deep learning based processing system designed to automatically delineate glacier fronts from multispectral Landsat imagery. The U-Net based framework was initially trained on 869 Greenland glacier front positions and is here extended by 252 front positions of the Antarctic Peninsula. The data product presented here includes 4817 calving front locations of 42 key outlet glaciers from 2013 to 2023 and achieves sub-seasonal temporal resolution. The mean difference between automated and manual extraction is estimated at 59.3 ± 5.9 m. This data set will help to better understand marine-terminating glacier dynamics on an intra-annual scale, study ice-ocean interactions in more detail and constrain glacier models. The data is publicly available at PANGAEA under https://doi.pangaea.de/10.1594/PANGAEA.963725 (Loebel et al., 2023)