Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first ro...

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Published in:Remote Sensing
Main Authors: Liling Liu, Xiaolong Dong, Wenming Lin, Shuyan Lang
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2023
Subjects:
Q
Online Access:https://doi.org/10.3390/rs15205063
https://doaj.org/article/20ae432efe204a03a809b5ed0a54d13a
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spelling ftdoajarticles:oai:doaj.org/article:20ae432efe204a03a809b5ed0a54d13a 2023-11-12T04:25:50+01:00 Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer Liling Liu Xiaolong Dong Wenming Lin Shuyan Lang 2023-10-01T00:00:00Z https://doi.org/10.3390/rs15205063 https://doaj.org/article/20ae432efe204a03a809b5ed0a54d13a EN eng MDPI AG https://www.mdpi.com/2072-4292/15/20/5063 https://doaj.org/toc/2072-4292 doi:10.3390/rs15205063 2072-4292 https://doaj.org/article/20ae432efe204a03a809b5ed0a54d13a Remote Sensing, Vol 15, Iss 5063, p 5063 (2023) scatterometer CSCAT sea ice detection Bayesian algorithm Science Q article 2023 ftdoajarticles https://doi.org/10.3390/rs15205063 2023-10-29T00:35:45Z Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 10 6 km 2 . Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Remote Sensing 15 20 5063
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic scatterometer
CSCAT
sea ice detection
Bayesian algorithm
Science
Q
spellingShingle scatterometer
CSCAT
sea ice detection
Bayesian algorithm
Science
Q
Liling Liu
Xiaolong Dong
Wenming Lin
Shuyan Lang
Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer
topic_facet scatterometer
CSCAT
sea ice detection
Bayesian algorithm
Science
Q
description Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 10 6 km 2 .
format Article in Journal/Newspaper
author Liling Liu
Xiaolong Dong
Wenming Lin
Shuyan Lang
author_facet Liling Liu
Xiaolong Dong
Wenming Lin
Shuyan Lang
author_sort Liling Liu
title Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer
title_short Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer
title_full Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer
title_fullStr Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer
title_full_unstemmed Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer
title_sort polar sea ice detection using a rotating fan beam scatterometer
publisher MDPI AG
publishDate 2023
url https://doi.org/10.3390/rs15205063
https://doaj.org/article/20ae432efe204a03a809b5ed0a54d13a
genre Sea ice
genre_facet Sea ice
op_source Remote Sensing, Vol 15, Iss 5063, p 5063 (2023)
op_relation https://www.mdpi.com/2072-4292/15/20/5063
https://doaj.org/toc/2072-4292
doi:10.3390/rs15205063
2072-4292
https://doaj.org/article/20ae432efe204a03a809b5ed0a54d13a
op_doi https://doi.org/10.3390/rs15205063
container_title Remote Sensing
container_volume 15
container_issue 20
container_start_page 5063
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