Validation of Sentinel-1-derived sea ice cover data in the Arctic

Abstract The Sentinel-1 synthetic aperture radar (SAR) instrument can image the polar regions with a large area and high temporal and spatial resolutions, which is particularly suitable for sea ice monitoring in the Arctic. In a previous study, a support vector machine (SVM)-based method was develop...

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Published in:IOP Conference Series: Earth and Environmental Science
Main Authors: Zhang, Qiang, Guo, Shibo, Sun, Yan, Dou, JianPing, Li, Xiao-Ming
Format: Article in Journal/Newspaper
Language:unknown
Published: IOP Publishing 2020
Subjects:
Online Access:http://dx.doi.org/10.1088/1755-1315/502/1/012032
https://iopscience.iop.org/article/10.1088/1755-1315/502/1/012032/pdf
https://iopscience.iop.org/article/10.1088/1755-1315/502/1/012032
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spelling crioppubl:10.1088/1755-1315/502/1/012032 2024-06-02T08:01:30+00:00 Validation of Sentinel-1-derived sea ice cover data in the Arctic Zhang, Qiang Guo, Shibo Sun, Yan Dou, JianPing Li, Xiao-Ming 2020 http://dx.doi.org/10.1088/1755-1315/502/1/012032 https://iopscience.iop.org/article/10.1088/1755-1315/502/1/012032/pdf https://iopscience.iop.org/article/10.1088/1755-1315/502/1/012032 unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining IOP Conference Series: Earth and Environmental Science volume 502, issue 1, page 012032 ISSN 1755-1307 1755-1315 journal-article 2020 crioppubl https://doi.org/10.1088/1755-1315/502/1/012032 2024-05-07T13:59:41Z Abstract The Sentinel-1 synthetic aperture radar (SAR) instrument can image the polar regions with a large area and high temporal and spatial resolutions, which is particularly suitable for sea ice monitoring in the Arctic. In a previous study, a support vector machine (SVM)-based method was developed to automatically extract the sea ice cover from Sentinel-1 SAR Extra Wide (EW) swath mode data in cross-polarization (horizontal-vertical, HV, or vertical-horizontal, VH). For validation, 468 Sentinel-1 EW HV-polarized images acquired in the Arctic are used to derive the sea ice cover with a spatial resolution of 0.96 km. We then compared the SAR-derived sea ice cover with the Ice Mapping System (IMS) dataset, which has a resolution of 1.0 km, and the AMSR-2 sea ice concentration (SIC, 15% is used as a threshold for deriving sea ice cover) dataset, which has a resolution of 3.125 km, based on pixel-by-pixel matching. The accuracies of the comparisons with the IMS and SIC data are 85.01% and 88.69%, respectively. The comparison shows that the IMS dataset is mostly overestimates the sea ice cover, while the AMSR2 dataset is relatively underestimates the sea ice extent. On the other hand, the sea ice cover information derived from the Sentinel-1 images by using the proposed method well characterizes the details of the marginal ice zone (MIZ). Furthermore, through the analysis of individual cases, the main factors that affect accuracy are drift (or pack) ice and a high land proportion in some areas. Article in Journal/Newspaper Arctic Sea ice IOP Publishing Arctic The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) IOP Conference Series: Earth and Environmental Science 502 012032
institution Open Polar
collection IOP Publishing
op_collection_id crioppubl
language unknown
description Abstract The Sentinel-1 synthetic aperture radar (SAR) instrument can image the polar regions with a large area and high temporal and spatial resolutions, which is particularly suitable for sea ice monitoring in the Arctic. In a previous study, a support vector machine (SVM)-based method was developed to automatically extract the sea ice cover from Sentinel-1 SAR Extra Wide (EW) swath mode data in cross-polarization (horizontal-vertical, HV, or vertical-horizontal, VH). For validation, 468 Sentinel-1 EW HV-polarized images acquired in the Arctic are used to derive the sea ice cover with a spatial resolution of 0.96 km. We then compared the SAR-derived sea ice cover with the Ice Mapping System (IMS) dataset, which has a resolution of 1.0 km, and the AMSR-2 sea ice concentration (SIC, 15% is used as a threshold for deriving sea ice cover) dataset, which has a resolution of 3.125 km, based on pixel-by-pixel matching. The accuracies of the comparisons with the IMS and SIC data are 85.01% and 88.69%, respectively. The comparison shows that the IMS dataset is mostly overestimates the sea ice cover, while the AMSR2 dataset is relatively underestimates the sea ice extent. On the other hand, the sea ice cover information derived from the Sentinel-1 images by using the proposed method well characterizes the details of the marginal ice zone (MIZ). Furthermore, through the analysis of individual cases, the main factors that affect accuracy are drift (or pack) ice and a high land proportion in some areas.
format Article in Journal/Newspaper
author Zhang, Qiang
Guo, Shibo
Sun, Yan
Dou, JianPing
Li, Xiao-Ming
spellingShingle Zhang, Qiang
Guo, Shibo
Sun, Yan
Dou, JianPing
Li, Xiao-Ming
Validation of Sentinel-1-derived sea ice cover data in the Arctic
author_facet Zhang, Qiang
Guo, Shibo
Sun, Yan
Dou, JianPing
Li, Xiao-Ming
author_sort Zhang, Qiang
title Validation of Sentinel-1-derived sea ice cover data in the Arctic
title_short Validation of Sentinel-1-derived sea ice cover data in the Arctic
title_full Validation of Sentinel-1-derived sea ice cover data in the Arctic
title_fullStr Validation of Sentinel-1-derived sea ice cover data in the Arctic
title_full_unstemmed Validation of Sentinel-1-derived sea ice cover data in the Arctic
title_sort validation of sentinel-1-derived sea ice cover data in the arctic
publisher IOP Publishing
publishDate 2020
url http://dx.doi.org/10.1088/1755-1315/502/1/012032
https://iopscience.iop.org/article/10.1088/1755-1315/502/1/012032/pdf
https://iopscience.iop.org/article/10.1088/1755-1315/502/1/012032
long_lat ENVELOPE(73.317,73.317,-52.983,-52.983)
geographic Arctic
The Sentinel
geographic_facet Arctic
The Sentinel
genre Arctic
Sea ice
genre_facet Arctic
Sea ice
op_source IOP Conference Series: Earth and Environmental Science
volume 502, issue 1, page 012032
ISSN 1755-1307 1755-1315
op_rights http://creativecommons.org/licenses/by/3.0/
https://iopscience.iop.org/info/page/text-and-data-mining
op_doi https://doi.org/10.1088/1755-1315/502/1/012032
container_title IOP Conference Series: Earth and Environmental Science
container_volume 502
container_start_page 012032
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