Articles | Volume 19, issue 1
https://doi.org/10.5194/tc-19-37-2025
© Author(s) 2025. 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-19-37-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jan 2025
Research article |
| 08 Jan 2025
| 08 Jan 2025
Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data
Weiran Li
CORRESPONDING AUTHOR
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands
Sanne B. M. Veldhuijsen
Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands
Stef Lhermitte
Department of Earth & Environmental Sciences, KU Leuven, Leuven, Belgium
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands
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Diana Francis, Kyle S. Mattingly, Stef Lhermitte, Marouane Temimi, and Petra Heil
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Thore Kausch, Stef Lhermitte, Jan T. M. Lenaerts, Nander Wever, Mana Inoue, Frank Pattyn, Sainan Sun, Sarah Wauthy, Jean-Louis Tison, and Willem Jan van de Berg
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Ice rises are elevated parts of the otherwise flat ice shelf. Here we study the impact of an Antarctic ice rise on the surrounding snow accumulation by combining field data and modeling. Our results show a clear difference in average yearly snow accumulation between the windward side, the leeward side and the peak of the ice rise due to differences in snowfall and wind erosion. This is relevant for the interpretation of ice core records, which are often drilled on the peak of an ice rise.
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Short summary
This study used a machine learning approach to estimate the densities over the Antarctic Ice Sheet, particularly in the areas where the snow is usually dry. The motivation is to establish a link between satellite parameters to snow densities, as measurements are difficult for people to take on site. It provides valuable insights into the complexities of the relationship between satellite parameters and firn density and provides potential for further studies.
This study used a machine learning approach to estimate the densities over the Antarctic Ice...
