EGU24-14894, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Towards monitoring supraglacial lake dynamics in Antarctica with convolutional neural networks

Celia A. Baumhoer1, Jonas Koehler1, Stef Lhermitte2,3, Bert Wouters2, and Andreas J. Dietz1
Celia A. Baumhoer et al.
  • 1German Aerospace Center, German Remote Sensing Data Center, Weßling, Germany (
  • 2Delft University of Technology, Department of Geoscience & Remote Sensing, Delft, Netherlands
  • 3KU Leuven, Department of Earth & Environmental Sciences, Leuven, Belgium

Monitoring the dynamics of Antarctic supraglacial lakes is of particular interest in the context of global warming. Supraglacial meltwater accumulation on ice sheets and ice shelves can be a major driver of accelerated ice discharge. This is caused through processes such as surface runoff leading to ice thinning, basal meltwater injection causing basal sliding, and hydrofracture triggering ice shelf collapse and subsequent glacier acceleration. In addition, an increased presence of supraglacial lakes around the Antarctic margin can trigger enhanced melting due to the low albedo of surface lakes, which leads to increased absorption of solar radiation. Hence, continuous monitoring of supraglacial lakes is crucial for improving our understanding of their seasonal variations in extent and their impacts on ice shelf stability and ice sheet surface mass balance. Initially, an automated supraglacial lake mapping approach was developed to create bi-weekly lake extent maps for six Antarctic ice shelves based on fused results from convolutional neural network predictions and a Random Forest (RF) classification trained on Sentinel-1/-2 data. However, regular large-scale monitoring beyond these six selected areas requires a model with higher spatio-temporal transferability and an efficient fully automated data processing workflow. We tested for a potential improvement by replacing the RF-based mapping with an attention-based U-Net, expanding the training and test sites on a total of 23 regions and switching the processing to a more powerful high-performance computing infrastructure. We will discuss how remote sensing-based mapping accuracies can be improved by extending the training/test dataset, selecting the right machine learning model and the choice of processing infrastructure. In the future, the automated processing workflow will provide a regularly updated dataset on supraglacial lake dynamics of 23 Antarctic ice shelves via a web service by exploiting the full archive of available Sentinel-1/-2 satellite imagery.

How to cite: Baumhoer, C. A., Koehler, J., Lhermitte, S., Wouters, B., and Dietz, A. J.: Towards monitoring supraglacial lake dynamics in Antarctica with convolutional neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14894,, 2024.