A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning
While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolutio...
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ftethz:oai:www.research-collection.ethz.ch:20.500.11850/647860 2024-02-11T09:58:13+01:00 A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning de Roda Husman, Sophie Lhermitte, Stef Bolibar, Jordi Izeboud, Maaike Hu, Zhongyang Shukla, Shashwat van der Meer, Marijn id_orcid:0 000-0002-7604-4494 Long, David Wouters, Bert 2024-02-01 application/application/pdf https://hdl.handle.net/20.500.11850/647860 https://doi.org/10.3929/ethz-b-000647860 en eng Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2023.113950 info:eu-repo/semantics/altIdentifier/wos/001134005200001 http://hdl.handle.net/20.500.11850/647860 doi:10.3929/ethz-b-000647860 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International Remote Sensing of Environment, 301 Antarctica U-Net Machine learning Microwave remote sensing Enhanced resolution Google Earth Engine Surface melt info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2024 ftethz https://doi.org/20.500.11850/64786010.3929/ethz-b-00064786010.1016/j.rse.2023.113950 2024-01-15T00:52:07Z While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing. ISSN:0034-4257 Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Shelf Ice Shelves Shackleton Ice Shelf ETH Zürich Research Collection Antarctic Shackleton Shackleton Ice Shelf ENVELOPE(100.504,100.504,-65.996,-65.996) |
institution |
Open Polar |
collection |
ETH Zürich Research Collection |
op_collection_id |
ftethz |
language |
English |
topic |
Antarctica U-Net Machine learning Microwave remote sensing Enhanced resolution Google Earth Engine Surface melt |
spellingShingle |
Antarctica U-Net Machine learning Microwave remote sensing Enhanced resolution Google Earth Engine Surface melt de Roda Husman, Sophie Lhermitte, Stef Bolibar, Jordi Izeboud, Maaike Hu, Zhongyang Shukla, Shashwat van der Meer, Marijn id_orcid:0 000-0002-7604-4494 Long, David Wouters, Bert A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
topic_facet |
Antarctica U-Net Machine learning Microwave remote sensing Enhanced resolution Google Earth Engine Surface melt |
description |
While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing. ISSN:0034-4257 |
format |
Article in Journal/Newspaper |
author |
de Roda Husman, Sophie Lhermitte, Stef Bolibar, Jordi Izeboud, Maaike Hu, Zhongyang Shukla, Shashwat van der Meer, Marijn id_orcid:0 000-0002-7604-4494 Long, David Wouters, Bert |
author_facet |
de Roda Husman, Sophie Lhermitte, Stef Bolibar, Jordi Izeboud, Maaike Hu, Zhongyang Shukla, Shashwat van der Meer, Marijn id_orcid:0 000-0002-7604-4494 Long, David Wouters, Bert |
author_sort |
de Roda Husman, Sophie |
title |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_short |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_full |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_fullStr |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_full_unstemmed |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_sort |
high-resolution record of surface melt on antarctic ice shelves using multi-source remote sensing data and deep learning |
publisher |
Elsevier |
publishDate |
2024 |
url |
https://hdl.handle.net/20.500.11850/647860 https://doi.org/10.3929/ethz-b-000647860 |
long_lat |
ENVELOPE(100.504,100.504,-65.996,-65.996) |
geographic |
Antarctic Shackleton Shackleton Ice Shelf |
geographic_facet |
Antarctic Shackleton Shackleton Ice Shelf |
genre |
Antarc* Antarctic Antarctica Ice Shelf Ice Shelves Shackleton Ice Shelf |
genre_facet |
Antarc* Antarctic Antarctica Ice Shelf Ice Shelves Shackleton Ice Shelf |
op_source |
Remote Sensing of Environment, 301 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2023.113950 info:eu-repo/semantics/altIdentifier/wos/001134005200001 http://hdl.handle.net/20.500.11850/647860 doi:10.3929/ethz-b-000647860 |
op_rights |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International |
op_doi |
https://doi.org/20.500.11850/64786010.3929/ethz-b-00064786010.1016/j.rse.2023.113950 |
_version_ |
1790593816146739200 |