Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3315-2024
https://doi.org/10.5194/tc-18-3315-2024
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

Viewed

Total article views: 1,386 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
925 402 59 1,386 17 50 36
  • HTML: 925
  • PDF: 402
  • XML: 59
  • Total: 1,386
  • Supplement: 17
  • BibTeX: 50
  • EndNote: 36
Views and downloads (calculated since 04 May 2023)
Cumulative views and downloads (calculated since 04 May 2023)

Viewed (geographical distribution)

Total article views: 1,386 (including HTML, PDF, and XML) Thereof 1,367 with geography defined and 19 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jul 2024
Download
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.