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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jul 2024
Research article |
| 24 Jul 2024
| 24 Jul 2024
Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Invited contribution by Erik Loebel, recipient of the EGU Cryospheric Sciences Virtual Outstanding Student and PhD candidate Presentation Award 2021.
Mirko Scheinert
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Martin Horwath
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Angelika Humbert
Sektion Glaziologie, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Fachbereich Geowissenschaften, Universität Bremen, Bremen, Germany
Julia Sohn
Sektion Glaziologie, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Professur für Engineering, IU Internationale Hochschule, Erfurt, Germany
Konrad Heidler
Chair of Data Science in Earth Observation, Technische Universität München, München, Germany
Charlotte Liebezeit
Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
Xiao Xiang Zhu
Chair of Data Science in Earth Observation, Technische Universität München, München, Germany
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Cited
11 citations as recorded by crossref.
- Active ice sheet conservation cannot stop the retreat of Sermeq Kujalleq glacier, Greenland L. Zhao et al. https://doi.org/10.1038/s43247-025-02120-8
- A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets W. Milczarek et al. https://doi.org/10.3390/rs18070969
- The system of atmosphere, land, ice and ocean in the region near the 79N Glacier in northeast Greenland: synthesis and key findings from the Greenland Ice Sheet–Ocean Interaction (GROCE) experiment T. Kanzow et al. https://doi.org/10.5194/tc-19-1789-2025
- A decade of summer glacier fronts and calved glacier ice distribution in Kongs- and Krossfjorden, Svalbard, observed by Sentinel-1 extra wide swath J. Haarpaintner et al. https://doi.org/10.1016/j.rse.2026.115693
- Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery E. Loebel et al. https://doi.org/10.5194/essd-17-65-2025
- Ice front positions for Greenland glaciers (2002–2021): a spatially extensive seasonal record and benchmark dataset for algorithm validation X. Lu et al. https://doi.org/10.5194/essd-18-2635-2026
- IW Extraction From SAR Images Based on Generative Networks With Small Datasets B. Saheya et al. https://doi.org/10.1109/JSTARS.2025.3576391
- Mélange, landfast sea ice, ice velocities: What controls seasonal calving rates in North West Greenland? S. Hedetoft et al. https://doi.org/10.5194/tc-20-5071-2026
- AMD-HookNet++: Evolution of AMD-HookNet With Hybrid CNN–Transformer Feature Enhancement for Glacier Calving Front Segmentation F. Wu et al. https://doi.org/10.1109/TGRS.2025.3642764
- From Spectral Indices to Artificial Intelligence: A Review of Remote Sensing Methodologies for Glacier Mapping A. Elzein et al. https://doi.org/10.3390/rs18101496
- A Novel Approach to Automated Mapping Subweekly Calving Front of Petermann Glacier (2016–2023)—Using Sentinel-2 Satellite Data and Segment Anything Model (SAM) D. Li et al. https://doi.org/10.1109/JSTARS.2025.3573496
11 citations as recorded by crossref.
- Active ice sheet conservation cannot stop the retreat of Sermeq Kujalleq glacier, Greenland L. Zhao et al. https://doi.org/10.1038/s43247-025-02120-8
- A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets W. Milczarek et al. https://doi.org/10.3390/rs18070969
- The system of atmosphere, land, ice and ocean in the region near the 79N Glacier in northeast Greenland: synthesis and key findings from the Greenland Ice Sheet–Ocean Interaction (GROCE) experiment T. Kanzow et al. https://doi.org/10.5194/tc-19-1789-2025
- A decade of summer glacier fronts and calved glacier ice distribution in Kongs- and Krossfjorden, Svalbard, observed by Sentinel-1 extra wide swath J. Haarpaintner et al. https://doi.org/10.1016/j.rse.2026.115693
- Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery E. Loebel et al. https://doi.org/10.5194/essd-17-65-2025
- Ice front positions for Greenland glaciers (2002–2021): a spatially extensive seasonal record and benchmark dataset for algorithm validation X. Lu et al. https://doi.org/10.5194/essd-18-2635-2026
- IW Extraction From SAR Images Based on Generative Networks With Small Datasets B. Saheya et al. https://doi.org/10.1109/JSTARS.2025.3576391
- Mélange, landfast sea ice, ice velocities: What controls seasonal calving rates in North West Greenland? S. Hedetoft et al. https://doi.org/10.5194/tc-20-5071-2026
- AMD-HookNet++: Evolution of AMD-HookNet With Hybrid CNN–Transformer Feature Enhancement for Glacier Calving Front Segmentation F. Wu et al. https://doi.org/10.1109/TGRS.2025.3642764
- From Spectral Indices to Artificial Intelligence: A Review of Remote Sensing Methodologies for Glacier Mapping A. Elzein et al. https://doi.org/10.3390/rs18101496
- A Novel Approach to Automated Mapping Subweekly Calving Front of Petermann Glacier (2016–2023)—Using Sentinel-2 Satellite Data and Segment Anything Model (SAM) D. Li et al. https://doi.org/10.1109/JSTARS.2025.3573496
Saved (final revised paper)
Latest update: 21 Oct 2026
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.
Comprehensive datasets of calving-front changes are essential for studying and modeling outlet...
