Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning
Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolu...
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Copernicus Publications
2021
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Online Access: | https://doi.org/10.5194/essd-13-2723-2021 https://essd.copernicus.org/articles/13/2723/2021/essd-13-2723-2021.pdf https://doaj.org/article/e1c6978a5c224140a696f460fe7a6346 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:e1c6978a5c224140a696f460fe7a6346 2023-05-15T15:02:19+02:00 Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning Y.-R. Wang X.-M. Li 2021-06-01 https://doi.org/10.5194/essd-13-2723-2021 https://essd.copernicus.org/articles/13/2723/2021/essd-13-2723-2021.pdf https://doaj.org/article/e1c6978a5c224140a696f460fe7a6346 en eng Copernicus Publications doi:10.5194/essd-13-2723-2021 1866-3508 1866-3516 https://essd.copernicus.org/articles/13/2723/2021/essd-13-2723-2021.pdf https://doaj.org/article/e1c6978a5c224140a696f460fe7a6346 undefined Earth System Science Data, Vol 13, Pp 2723-2742 (2021) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.5194/essd-13-2723-2021 2023-01-22T19:30:57Z Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high-resolution sea ice cover product for the Arctic using Sentinel-1 (S1) dual-polarization (horizontal-horizontal, HH, and horizontal-vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U-Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U-Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28 000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. The validation by 96 cases of visual interpretation results shows an overall accuracy of 96.10 %. The S1-derived sea ice cover was converted to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 % with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 %. These results show that the developed S1-derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ). Data are available at https://doi.org/10.11922/sciencedb.00273 (Wang and Li, 2020). Article in Journal/Newspaper Arctic Sea ice Unknown Arctic Earth System Science Data 13 6 2723 2742 |
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geo envir Y.-R. Wang X.-M. Li Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning |
topic_facet |
geo envir |
description |
Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high-resolution sea ice cover product for the Arctic using Sentinel-1 (S1) dual-polarization (horizontal-horizontal, HH, and horizontal-vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U-Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U-Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28 000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. The validation by 96 cases of visual interpretation results shows an overall accuracy of 96.10 %. The S1-derived sea ice cover was converted to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 % with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 %. These results show that the developed S1-derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ). Data are available at https://doi.org/10.11922/sciencedb.00273 (Wang and Li, 2020). |
format |
Article in Journal/Newspaper |
author |
Y.-R. Wang X.-M. Li |
author_facet |
Y.-R. Wang X.-M. Li |
author_sort |
Y.-R. Wang |
title |
Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning |
title_short |
Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning |
title_full |
Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning |
title_fullStr |
Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning |
title_full_unstemmed |
Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning |
title_sort |
arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/essd-13-2723-2021 https://essd.copernicus.org/articles/13/2723/2021/essd-13-2723-2021.pdf https://doaj.org/article/e1c6978a5c224140a696f460fe7a6346 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Earth System Science Data, Vol 13, Pp 2723-2742 (2021) |
op_relation |
doi:10.5194/essd-13-2723-2021 1866-3508 1866-3516 https://essd.copernicus.org/articles/13/2723/2021/essd-13-2723-2021.pdf https://doaj.org/article/e1c6978a5c224140a696f460fe7a6346 |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/essd-13-2723-2021 |
container_title |
Earth System Science Data |
container_volume |
13 |
container_issue |
6 |
container_start_page |
2723 |
op_container_end_page |
2742 |
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1766334282651926528 |