A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic

NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To furth...

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Published in:Remote Sensing
Main Authors: Jun Liu, Huan Xie, Yalei Guo, Xiaohua Tong, Peinan Li
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://doi.org/10.3390/rs14051130
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spelling ftmdpi:oai:mdpi.com:/2072-4292/14/5/1130/ 2023-08-20T04:03:59+02:00 A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic Jun Liu Huan Xie Yalei Guo Xiaohua Tong Peinan Li 2022-02-24 application/pdf https://doi.org/10.3390/rs14051130 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs14051130 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 5; Pages: 1130 arctic ICESat-2 sea ice concentration polar oceans lidars Text 2022 ftmdpi https://doi.org/10.3390/rs14051130 2023-08-01T04:17:00Z NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve the detection accuracy of the sea ice concentration (SIC), we demonstrate a new processing chain that can be used to convert the along-track sea ice freeboard products (ATL10) obtained by ICESat-2 into the SIC, with our initial efforts being focused on the Arctic. For this conversion, we primarily make use of the classification results from the type (sea ice or lead) and segment length data gathered from ATL10. The along-track SIC is the ratio of the area that is covered by sea ice segments to the area of all of the along-track segments. We generated a monthly gridded SIC product with a 25 km resolution and compared this to the NSIDC Climate Data Record (CDR) sea ice concentration. The highest correlation was determined to be 0.7690 in September at high latitudes and the lowest correlation was found to be 0.8595 in June at mid-latitudes. The regions with large standard deviations in summer and autumn are mainly distributed in the thin-ice areas at mid-latitudes. In the Laptev Sea and Kara Sea of east Siberia, the differences in the standard deviation were large; the maximum bias was −0.1566, in November, and the minimum bias was −0.0216, in June. ICESat-2 shows great potential for the accurate estimation of the SIC. Text Arctic Kara Sea laptev Laptev Sea Sea ice Siberia MDPI Open Access Publishing Arctic Laptev Sea Kara Sea Remote Sensing 14 5 1130
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic arctic
ICESat-2
sea ice concentration
polar oceans
lidars
spellingShingle arctic
ICESat-2
sea ice concentration
polar oceans
lidars
Jun Liu
Huan Xie
Yalei Guo
Xiaohua Tong
Peinan Li
A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic
topic_facet arctic
ICESat-2
sea ice concentration
polar oceans
lidars
description NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve the detection accuracy of the sea ice concentration (SIC), we demonstrate a new processing chain that can be used to convert the along-track sea ice freeboard products (ATL10) obtained by ICESat-2 into the SIC, with our initial efforts being focused on the Arctic. For this conversion, we primarily make use of the classification results from the type (sea ice or lead) and segment length data gathered from ATL10. The along-track SIC is the ratio of the area that is covered by sea ice segments to the area of all of the along-track segments. We generated a monthly gridded SIC product with a 25 km resolution and compared this to the NSIDC Climate Data Record (CDR) sea ice concentration. The highest correlation was determined to be 0.7690 in September at high latitudes and the lowest correlation was found to be 0.8595 in June at mid-latitudes. The regions with large standard deviations in summer and autumn are mainly distributed in the thin-ice areas at mid-latitudes. In the Laptev Sea and Kara Sea of east Siberia, the differences in the standard deviation were large; the maximum bias was −0.1566, in November, and the minimum bias was −0.0216, in June. ICESat-2 shows great potential for the accurate estimation of the SIC.
format Text
author Jun Liu
Huan Xie
Yalei Guo
Xiaohua Tong
Peinan Li
author_facet Jun Liu
Huan Xie
Yalei Guo
Xiaohua Tong
Peinan Li
author_sort Jun Liu
title A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic
title_short A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic
title_full A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic
title_fullStr A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic
title_full_unstemmed A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic
title_sort sea ice concentration estimation methodology utilizing icesat-2 photon-counting laser altimeter in the arctic
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/rs14051130
geographic Arctic
Laptev Sea
Kara Sea
geographic_facet Arctic
Laptev Sea
Kara Sea
genre Arctic
Kara Sea
laptev
Laptev Sea
Sea ice
Siberia
genre_facet Arctic
Kara Sea
laptev
Laptev Sea
Sea ice
Siberia
op_source Remote Sensing; Volume 14; Issue 5; Pages: 1130
op_relation Remote Sensing in Geology, Geomorphology and Hydrology
https://dx.doi.org/10.3390/rs14051130
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs14051130
container_title Remote Sensing
container_volume 14
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