Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data
Abstract: Supraglacial lakes are an important parameter for understanding the current and future state of the Greenland Ice Sheet. Previous studies have focused on mapping supraglacial lake extent using optical and radar imagery, while lake depth is more difficult to estimate due to sparse temporal...
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ftzenodo:oai:zenodo.org:7981531 2024-09-15T18:08:58+00:00 Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data Abigail Elizabeth Robinson David Völgyes Martijn Vermeer Daniele Stefano Maria Fantin Louise Sandberg Sørensen Mikkel Aaby Kruse Sabine Frosch 2023-05-29 https://doi.org/10.5281/zenodo.7981531 eng eng Zenodo https://doi.org/10.5281/zenodo.7981530 https://doi.org/10.5281/zenodo.7981531 oai:zenodo.org:7981531 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode GLOC 2023, Global Space Conference on climate change 2023, Oslo, Norway, 23-25 May, 2023 info:eu-repo/semantics/conferencePaper 2023 ftzenodo https://doi.org/10.5281/zenodo.798153110.5281/zenodo.7981530 2024-07-26T07:18:57Z Abstract: Supraglacial lakes are an important parameter for understanding the current and future state of the Greenland Ice Sheet. Previous studies have focused on mapping supraglacial lake extent using optical and radar imagery, while lake depth is more difficult to estimate due to sparse temporal and spatial coverage of laser altimeters such as ICESat-2. We present a supervised deep learning approach to predict lake extent and depth based on the subtle spectral signatures acquired from Sentinel2 imagery. The model is trained on an existing lake extent product and elevation profiles derived from ICESat-2. The output of this approach is a proof-of-concept study whereby deep learning can utilise contextual information from the input image to produce a lake depth and extent prediction. The preliminary results show that the methodology is feasible as the output model successfully produces a reasonable lake extent and depth prediction despite data limitations. This work forms part of the European Space Agency’s Greenland Ice Sheet Climate Change Initiative (ESA GIS CCI+), which runs from December 2022 until 2025. Conference Object Greenland Ice Sheet Zenodo |
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Abstract: Supraglacial lakes are an important parameter for understanding the current and future state of the Greenland Ice Sheet. Previous studies have focused on mapping supraglacial lake extent using optical and radar imagery, while lake depth is more difficult to estimate due to sparse temporal and spatial coverage of laser altimeters such as ICESat-2. We present a supervised deep learning approach to predict lake extent and depth based on the subtle spectral signatures acquired from Sentinel2 imagery. The model is trained on an existing lake extent product and elevation profiles derived from ICESat-2. The output of this approach is a proof-of-concept study whereby deep learning can utilise contextual information from the input image to produce a lake depth and extent prediction. The preliminary results show that the methodology is feasible as the output model successfully produces a reasonable lake extent and depth prediction despite data limitations. This work forms part of the European Space Agency’s Greenland Ice Sheet Climate Change Initiative (ESA GIS CCI+), which runs from December 2022 until 2025. |
format |
Conference Object |
author |
Abigail Elizabeth Robinson David Völgyes Martijn Vermeer Daniele Stefano Maria Fantin Louise Sandberg Sørensen Mikkel Aaby Kruse Sabine Frosch |
spellingShingle |
Abigail Elizabeth Robinson David Völgyes Martijn Vermeer Daniele Stefano Maria Fantin Louise Sandberg Sørensen Mikkel Aaby Kruse Sabine Frosch Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data |
author_facet |
Abigail Elizabeth Robinson David Völgyes Martijn Vermeer Daniele Stefano Maria Fantin Louise Sandberg Sørensen Mikkel Aaby Kruse Sabine Frosch |
author_sort |
Abigail Elizabeth Robinson |
title |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data |
title_short |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data |
title_full |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data |
title_fullStr |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data |
title_full_unstemmed |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data |
title_sort |
deep learning-based supraglacial lake extent and depth detection on the greenland ice sheet by combining icesat-2 and sentinel-2 data |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://doi.org/10.5281/zenodo.7981531 |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
GLOC 2023, Global Space Conference on climate change 2023, Oslo, Norway, 23-25 May, 2023 |
op_relation |
https://doi.org/10.5281/zenodo.7981530 https://doi.org/10.5281/zenodo.7981531 oai:zenodo.org:7981531 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.798153110.5281/zenodo.7981530 |
_version_ |
1810446352922968064 |