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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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 |
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Open Polar |
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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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1766189963716591616 |