Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network
In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the c...
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ftdoajarticles:oai:doaj.org/article:eea0556c3e88491bb4349d2322173cc6 2023-05-15T14:51:55+02:00 Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network Xiao-Ming Li Tingting Qin Ke Wu 2020-10-01T00:00:00Z https://doi.org/10.3390/rs12203291 https://doaj.org/article/eea0556c3e88491bb4349d2322173cc6 EN eng MDPI AG https://www.mdpi.com/2072-4292/12/20/3291 https://doaj.org/toc/2072-4292 doi:10.3390/rs12203291 2072-4292 https://doaj.org/article/eea0556c3e88491bb4349d2322173cc6 Remote Sensing, Vol 12, Iss 3291, p 3291 (2020) synthetic aperture radar sea surface wind machine learning Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12203291 2022-12-31T00:44:41Z In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic. Article in Journal/Newspaper Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 12 20 3291 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
synthetic aperture radar sea surface wind machine learning Science Q |
spellingShingle |
synthetic aperture radar sea surface wind machine learning Science Q Xiao-Ming Li Tingting Qin Ke Wu Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network |
topic_facet |
synthetic aperture radar sea surface wind machine learning Science Q |
description |
In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic. |
format |
Article in Journal/Newspaper |
author |
Xiao-Ming Li Tingting Qin Ke Wu |
author_facet |
Xiao-Ming Li Tingting Qin Ke Wu |
author_sort |
Xiao-Ming Li |
title |
Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network |
title_short |
Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network |
title_full |
Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network |
title_fullStr |
Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network |
title_full_unstemmed |
Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network |
title_sort |
retrieval of sea surface wind speed from spaceborne sar over the arctic marginal ice zone with a neural network |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12203291 https://doaj.org/article/eea0556c3e88491bb4349d2322173cc6 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing, Vol 12, Iss 3291, p 3291 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/20/3291 https://doaj.org/toc/2072-4292 doi:10.3390/rs12203291 2072-4292 https://doaj.org/article/eea0556c3e88491bb4349d2322173cc6 |
op_doi |
https://doi.org/10.3390/rs12203291 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
20 |
container_start_page |
3291 |
_version_ |
1766323063999168512 |