Preprints
https://doi.org/10.5194/essd-2023-535
https://doi.org/10.5194/essd-2023-535
02 Feb 2024
 | 02 Feb 2024
Status: this preprint is currently under review for the journal ESSD.

Calving front positions for 19 key glaciers of the Antarctic Peninsula: a sub-seasonal record from 2013 to 2023 based on a deep learning application to Landsat multispectral imagery

Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath

Abstract. Calving front positions of marine-terminating glaciers are an essential parameter to 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 being 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 236 front positions of the Antarctic Peninsula. The here presented data product includes 2064 calving front locations of 19 key outlet glaciers from 2013 to 2023 and achieves sub-seasonal temporal resolution. 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., 2023b).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-535', Benjamin Davison, 28 Feb 2024
    • AC1: 'Reply on RC1', Erik Loebel, 26 May 2024
  • RC2: 'Comment on essd-2023-535', Anonymous Referee #2, 11 Mar 2024
    • AC2: 'Reply on RC2', Erik Loebel, 26 May 2024
Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath

Data sets

Glacier calving front locations for the Antarctic Peninsula derived from remote sensing and deep learning from 2013 to 2023 Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath https://doi.pangaea.de/10.1594/PANGAEA.963725

Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath

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Short summary
Glacier calving front positions are important for understanding glacier dynamics and constrain ice modelling. We apply a deep learning framework on multispectral Landsat imagery to create a calving front record for 19 key outlet glaciers of the Antarctic Peninsula. The resulting data product includes 2064 calving front locations from 2013 to 2023 and achieves sub-seasonal temporal resolution.
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