04 May 2023
 | 04 May 2023
Status: this preprint is currently under review for the journal TC.

Calving front monitoring at sub-seasonal resolution: a deep learning application to Greenland glaciers

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

Abstract. The mass balance of the Greenland ice sheet is strongly influenced by the dynamics of its outlet glaciers. Therefore, it is of paramount importance to accurately and continuously monitor these glaciers, especially the variation of their frontal positions. A temporally comprehensive parameterization of glacier calving is essential to understand dynamic changes and to constrain ice sheet modelling. However, current calving front records are often limited in temporal resolution as they rely on manual delineation, which is laborious and not feasible with the increasing amount of satellite imagery available. In this contribution, we address this problem by applying an automated method to extract calving fronts from optical satellite imagery. The core of this workflow builds on recent advances in the field of deep learning while taking full advantage of multispectral input information. The performance of the method is evaluated using three independent validation datasets. Eventually, we apply the technique to Landsat-8 imagery. We generate 9243 calving front positions across 23 Greenland outlet glaciers from 2013 to 2021. Resulting time series resolve not only long-term and seasonal signals but also sub-seasonal patterns. We discuss the implications for glaciological studies and present a first application analysing the interaction between calving front variation and bedrock topography. Our method and derived results represent an important step towards the development of intelligent processing strategies for glacier monitoring, opening up new possibilities for studying and modelling the dynamics of Greenland outlet glaciers. Thus, these also contribute to advance the construction of a digital twin of the Greenland ice sheet, which will improve our understanding of its evolution and role within the Earth's climate system.

Erik Loebel et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-52', Anonymous Referee #1, 01 Jun 2023
    • AC1: 'Reply on RC1', Erik Loebel, 01 Dec 2023
  • RC2: 'Comment on tc-2023-52', Anonymous Referee #2, 05 Jun 2023
    • AC2: 'Reply on RC2', Erik Loebel, 01 Dec 2023
  • RC3: 'Comment on tc-2023-52', Anonymous Referee #3, 18 Oct 2023
    • AC3: 'Reply on RC3', Erik Loebel, 01 Dec 2023

Erik Loebel et al.

Erik Loebel et al.


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Short summary
Comprehensive data sets of calving front change are essential to study and model outlet glaciers. Current records are limited in temporal resolution as they rely on manual delineation. We apply deep learning to automatically delineate calving fronts of 23 Greenland glaciers. Resulting time series resolve long-term, seasonal and sub-seasonal patterns. We discuss the implications of our results and provide the cryosphere community with a data product and an implementation of our processing system.