Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic
Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the...
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Multidisciplinary Digital Publishing Institute
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Online Access: | https://doi.org/10.3390/rs15030678 |
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ftmdpi:oai:mdpi.com:/2072-4292/15/3/678/ 2023-08-20T04:03:42+02:00 Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic Lu Han Haihua Chen Lei Guan Lele Li agris 2023-01-23 application/pdf https://doi.org/10.3390/rs15030678 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs15030678 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 3; Pages: 678 HY-2B/SCA sea ice type AARI Arctic stacking model decision trees image segmentation Text 2023 ftmdpi https://doi.org/10.3390/rs15030678 2023-08-01T08:26:42Z Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the HaiYang-2B Scatterometer (HY-2B SCA). First, the parameters most applicable to classify sea ice type are selected through feature extraction, and a stacking model is established for the first time, which integrates decision tree and image segmentation algorithms. Finally, multiple sea ice types are classified in the Arctic, comprising Nilas, Young Ice, First Year Ice, Old Ice, and Fast Ice. Comparing the results with the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Ice Type dataset (SIT) indicates that the sea ice type classified by HY-2B SCA (Stacking-HY2B) is similar to OSI-SAF SIT with regard to the changing trends in extent of sea ice. We use the Copernicus Marine Environment Monitoring Service (CMEMS) high-resolution sea ice type data and EM-Bird ice thickness data to validate the result, and accuracies of 87% and 88% are obtained, respectively. This indicates that the algorithm in this work is comparable with the performance of OSI-SAF dataset, while providing information of multiple sea ice types. Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 15 3 678 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
HY-2B/SCA sea ice type AARI Arctic stacking model decision trees image segmentation |
spellingShingle |
HY-2B/SCA sea ice type AARI Arctic stacking model decision trees image segmentation Lu Han Haihua Chen Lei Guan Lele Li Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic |
topic_facet |
HY-2B/SCA sea ice type AARI Arctic stacking model decision trees image segmentation |
description |
Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the HaiYang-2B Scatterometer (HY-2B SCA). First, the parameters most applicable to classify sea ice type are selected through feature extraction, and a stacking model is established for the first time, which integrates decision tree and image segmentation algorithms. Finally, multiple sea ice types are classified in the Arctic, comprising Nilas, Young Ice, First Year Ice, Old Ice, and Fast Ice. Comparing the results with the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Ice Type dataset (SIT) indicates that the sea ice type classified by HY-2B SCA (Stacking-HY2B) is similar to OSI-SAF SIT with regard to the changing trends in extent of sea ice. We use the Copernicus Marine Environment Monitoring Service (CMEMS) high-resolution sea ice type data and EM-Bird ice thickness data to validate the result, and accuracies of 87% and 88% are obtained, respectively. This indicates that the algorithm in this work is comparable with the performance of OSI-SAF dataset, while providing information of multiple sea ice types. |
format |
Text |
author |
Lu Han Haihua Chen Lei Guan Lele Li |
author_facet |
Lu Han Haihua Chen Lei Guan Lele Li |
author_sort |
Lu Han |
title |
Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic |
title_short |
Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic |
title_full |
Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic |
title_fullStr |
Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic |
title_full_unstemmed |
Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic |
title_sort |
multiple sea ice type retrieval using the haiyang-2b scatterometer in the arctic |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15030678 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing; Volume 15; Issue 3; Pages: 678 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs15030678 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15030678 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
3 |
container_start_page |
678 |
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1774714141556080640 |