Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflect...

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Autores principales:	Yun Zhang, Xu Chen, Wanting Meng, Jiwei Yin, Yanling Han, Zhonghua Hong, Shuhu Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GNSS-R wind direction CYGNSS SVM DDM Science Q
Acceso en línea:	https://doaj.org/article/46ffa8db561a4de489bd8a383ab32b6f
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id	oai:doaj.org-article:46ffa8db561a4de489bd8a383ab32b6f
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spelling	oai:doaj.org-article:46ffa8db561a4de489bd8a383ab32b6f2021-11-11T18:57:22ZWind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data10.3390/rs132144512072-4292https://doaj.org/article/46ffa8db561a4de489bd8a383ab32b6f2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4451https://doaj.org/toc/2072-4292In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.Yun ZhangXu ChenWanting MengJiwei YinYanling HanZhonghua HongShuhu YangMDPI AGarticleGNSS-Rwind directionCYGNSSSVMDDMScienceQENRemote Sensing, Vol 13, Iss 4451, p 4451 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GNSS-R wind direction CYGNSS SVM DDM Science Q
spellingShingle	GNSS-R wind direction CYGNSS SVM DDM Science Q Yun Zhang Xu Chen Wanting Meng Jiwei Yin Yanling Han Zhonghua Hong Shuhu Yang Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
description	In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.
format	article
author	Yun Zhang Xu Chen Wanting Meng Jiwei Yin Yanling Han Zhonghua Hong Shuhu Yang
author_facet	Yun Zhang Xu Chen Wanting Meng Jiwei Yin Yanling Han Zhonghua Hong Shuhu Yang
author_sort	Yun Zhang
title	Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
title_short	Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
title_full	Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
title_fullStr	Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
title_full_unstemmed	Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
title_sort	wind direction retrieval using support vector machine from cygnss sea surface data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/46ffa8db561a4de489bd8a383ab32b6f
work_keys_str_mv	AT yunzhang winddirectionretrievalusingsupportvectormachinefromcygnssseasurfacedata AT xuchen winddirectionretrievalusingsupportvectormachinefromcygnssseasurfacedata AT wantingmeng winddirectionretrievalusingsupportvectormachinefromcygnssseasurfacedata AT jiweiyin winddirectionretrievalusingsupportvectormachinefromcygnssseasurfacedata AT yanlinghan winddirectionretrievalusingsupportvectormachinefromcygnssseasurfacedata AT zhonghuahong winddirectionretrievalusingsupportvectormachinefromcygnssseasurfacedata AT shuhuyang winddirectionretrievalusingsupportvectormachinefromcygnssseasurfacedata
_version_	1718431642021789696

Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data

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