Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager

Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlat...

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Published in:Sensors
Main Authors: Ni, Kun, Chen, Haihua, Li, Lele, Meng, Xin
Format: Text
Language:English
Published: MDPI 2022
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655684/
http://www.ncbi.nlm.nih.gov/pubmed/36365995
https://doi.org/10.3390/s22218298
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spelling ftpubmed:oai:pubmedcentral.nih.gov:9655684 2023-05-15T14:59:09+02:00 Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager Ni, Kun Chen, Haihua Li, Lele Meng, Xin 2022-10-29 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655684/ http://www.ncbi.nlm.nih.gov/pubmed/36365995 https://doi.org/10.3390/s22218298 en eng MDPI http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655684/ http://www.ncbi.nlm.nih.gov/pubmed/36365995 http://dx.doi.org/10.3390/s22218298 © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). CC-BY Sensors (Basel) Article Text 2022 ftpubmed https://doi.org/10.3390/s22218298 2022-11-20T02:31:21Z Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlation (MCC) and the successive correction method (SCM). The proposed method can apply different scales of search ranges to template matching according to the location of sea ice in the Arctic area. In addition, the data assimilation method can assign different weights to different data. We used 36.5 GHz and 89 GHz brightness temperature (T(b)) data from the microwave radiometer imager (MWRI) aboard the Fengyun-3D (FY-3D) satellite, for the first time in the literature, to retrieve the sea ice motion in the Beaufort Sea from January to April 2019. The retrieved sea ice motion results were in good agreement with those obtained from the motion of the buoys. Compared with the data from the buoys, the root mean-squared error (RMSE) of the sea ice motion retrieved from FY-3D/MWRI [Formula: see text] data was 1.1418 cm/s in the zonal direction and 1.0481 cm/s in the meridional direction, and the mean absolute error (MAE) between them was 0.7166 cm/s in the zonal direction and 0.6777 cm/s in the meridional direction. The RMSE between the sea ice motion obtained from the National Snow and Ice Data Center (NSIDC) and the motion of the buoys was 0.9515 cm/s in the zonal direction and 0.67003 cm/s in the meridional direction, and the MAE between them was 0.6576 cm/s in the zonal direction and 0.4922 cm/s in the meridional direction. The [Formula: see text] of daily average velocity from the FY-3D/MWRI results and NSIDC data product was 2.2726 cm/s in zonal and 1.9270 cm/s in meridional, and the MAE was 1.5103 cm/s in zonal and 1.1071 cm/s in zonal. The density of the merged data was higher than that obtained from a single polarization or frequency in this paper. The results indicate that FY-3D/MWRI T(b) data can retrieve the sea ... Text Arctic Beaufort Sea Global warming National Snow and Ice Data Center Sea ice PubMed Central (PMC) Arctic Sensors 22 21 8298
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Article
spellingShingle Article
Ni, Kun
Chen, Haihua
Li, Lele
Meng, Xin
Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager
topic_facet Article
description Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlation (MCC) and the successive correction method (SCM). The proposed method can apply different scales of search ranges to template matching according to the location of sea ice in the Arctic area. In addition, the data assimilation method can assign different weights to different data. We used 36.5 GHz and 89 GHz brightness temperature (T(b)) data from the microwave radiometer imager (MWRI) aboard the Fengyun-3D (FY-3D) satellite, for the first time in the literature, to retrieve the sea ice motion in the Beaufort Sea from January to April 2019. The retrieved sea ice motion results were in good agreement with those obtained from the motion of the buoys. Compared with the data from the buoys, the root mean-squared error (RMSE) of the sea ice motion retrieved from FY-3D/MWRI [Formula: see text] data was 1.1418 cm/s in the zonal direction and 1.0481 cm/s in the meridional direction, and the mean absolute error (MAE) between them was 0.7166 cm/s in the zonal direction and 0.6777 cm/s in the meridional direction. The RMSE between the sea ice motion obtained from the National Snow and Ice Data Center (NSIDC) and the motion of the buoys was 0.9515 cm/s in the zonal direction and 0.67003 cm/s in the meridional direction, and the MAE between them was 0.6576 cm/s in the zonal direction and 0.4922 cm/s in the meridional direction. The [Formula: see text] of daily average velocity from the FY-3D/MWRI results and NSIDC data product was 2.2726 cm/s in zonal and 1.9270 cm/s in meridional, and the MAE was 1.5103 cm/s in zonal and 1.1071 cm/s in zonal. The density of the merged data was higher than that obtained from a single polarization or frequency in this paper. The results indicate that FY-3D/MWRI T(b) data can retrieve the sea ...
format Text
author Ni, Kun
Chen, Haihua
Li, Lele
Meng, Xin
author_facet Ni, Kun
Chen, Haihua
Li, Lele
Meng, Xin
author_sort Ni, Kun
title Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager
title_short Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager
title_full Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager
title_fullStr Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager
title_full_unstemmed Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager
title_sort retrieving the motion of beaufort sea ice using brightness temperature data from fy-3d microwave radiometer imager
publisher MDPI
publishDate 2022
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655684/
http://www.ncbi.nlm.nih.gov/pubmed/36365995
https://doi.org/10.3390/s22218298
geographic Arctic
geographic_facet Arctic
genre Arctic
Beaufort Sea
Global warming
National Snow and Ice Data Center
Sea ice
genre_facet Arctic
Beaufort Sea
Global warming
National Snow and Ice Data Center
Sea ice
op_source Sensors (Basel)
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655684/
http://www.ncbi.nlm.nih.gov/pubmed/36365995
http://dx.doi.org/10.3390/s22218298
op_rights © 2022 by the authors.
https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
op_rightsnorm CC-BY
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container_title Sensors
container_volume 22
container_issue 21
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