Bayesian Sea Ice Detection Algorithm for CFOSAT

This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is...

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Published in:Remote Sensing
Main Authors: Zhen Li, Anton Verhoef, Ad Stoffelen
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
Q
Online Access:https://doi.org/10.3390/rs14153569
https://doaj.org/article/51e0ad6cdcb34fc1a44f9a12bd42e996
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spelling ftdoajarticles:oai:doaj.org/article:51e0ad6cdcb34fc1a44f9a12bd42e996 2023-05-15T18:16:23+02:00 Bayesian Sea Ice Detection Algorithm for CFOSAT Zhen Li Anton Verhoef Ad Stoffelen 2022-07-01T00:00:00Z https://doi.org/10.3390/rs14153569 https://doaj.org/article/51e0ad6cdcb34fc1a44f9a12bd42e996 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/15/3569 https://doaj.org/toc/2072-4292 doi:10.3390/rs14153569 2072-4292 https://doaj.org/article/51e0ad6cdcb34fc1a44f9a12bd42e996 Remote Sensing, Vol 14, Iss 3569, p 3569 (2022) sea ice Bayesian algorithm CFOSAT scatterometer Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14153569 2022-12-30T20:39:44Z This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea; (2) a linear ice GMF as a function of incidence angle; (3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around −12 dB (28) and −20 dB (50) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as the algorithm was adapted for a rotating fan-beam ... Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Remote Sensing 14 15 3569
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic sea ice
Bayesian algorithm
CFOSAT
scatterometer
Science
Q
spellingShingle sea ice
Bayesian algorithm
CFOSAT
scatterometer
Science
Q
Zhen Li
Anton Verhoef
Ad Stoffelen
Bayesian Sea Ice Detection Algorithm for CFOSAT
topic_facet sea ice
Bayesian algorithm
CFOSAT
scatterometer
Science
Q
description This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea; (2) a linear ice GMF as a function of incidence angle; (3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around −12 dB (28) and −20 dB (50) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as the algorithm was adapted for a rotating fan-beam ...
format Article in Journal/Newspaper
author Zhen Li
Anton Verhoef
Ad Stoffelen
author_facet Zhen Li
Anton Verhoef
Ad Stoffelen
author_sort Zhen Li
title Bayesian Sea Ice Detection Algorithm for CFOSAT
title_short Bayesian Sea Ice Detection Algorithm for CFOSAT
title_full Bayesian Sea Ice Detection Algorithm for CFOSAT
title_fullStr Bayesian Sea Ice Detection Algorithm for CFOSAT
title_full_unstemmed Bayesian Sea Ice Detection Algorithm for CFOSAT
title_sort bayesian sea ice detection algorithm for cfosat
publisher MDPI AG
publishDate 2022
url https://doi.org/10.3390/rs14153569
https://doaj.org/article/51e0ad6cdcb34fc1a44f9a12bd42e996
genre Sea ice
genre_facet Sea ice
op_source Remote Sensing, Vol 14, Iss 3569, p 3569 (2022)
op_relation https://www.mdpi.com/2072-4292/14/15/3569
https://doaj.org/toc/2072-4292
doi:10.3390/rs14153569
2072-4292
https://doaj.org/article/51e0ad6cdcb34fc1a44f9a12bd42e996
op_doi https://doi.org/10.3390/rs14153569
container_title Remote Sensing
container_volume 14
container_issue 15
container_start_page 3569
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