The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 7
Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3315-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-3315-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 7
Research article
 | 
24 Jul 2024
Research article |  | 24 Jul 2024

Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers

Erik Loebel, Mirko Scheinert, Martin Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, and Xiao Xiang Zhu

Supplement

https://doi.org/10.5194/tc-18-3315-2024-supplement

Data sets

Data product of Greenland glacier calving front locations delineated by deep learning, 2013 to 2021 E. Loebel et al. https://doi.org/10.25532/OPARA-208

Manually delineated glacier calving front locations of 27 marine-terminating glaciers from 2013 to 2021 E. Loebel et al. https://doi.org/10.25532/OPARA-282

Model code and software

eloebel/glacier-front-extraction: Initial release v1.0.0 E. Loebel https://doi.org/10.5281/zenodo.7755774

eloebel/rectilinear-box-method: Initial release v1.0.0 E. Loebel https://doi.org/10.5281/zenodo.7738605

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Short summary
Comprehensive datasets of calving-front changes are essential for studying and modeling outlet glaciers. Current records are limited in temporal resolution due to manual delineation. We use deep learning to automatically delineate calving fronts for 23 glaciers in Greenland. Resulting time series resolve long-term, seasonal, and subseasonal patterns. We discuss the implications of our results and provide the cryosphere community with a data product and an implementation of our processing system.
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