WIND RETRIEVALFOR CFOSCAT EDGE AND NADIR OBSERVATIONS BASED ON NEURAL NETWORKS AND IMPROVED PRINCIPLE COMPONENT ANALYSIS

NSSC OpenIR

	WIND RETRIEVALFOR CFOSCAT EDGE AND NADIR OBSERVATIONS BASED ON NEURAL NETWORKS AND IMPROVED PRINCIPLE COMPONENT ANALYSIS
Alternative Title	WOS:000519270607118
	Xu, Xingou ; Stoffelen, Ad 1
Source Publication	2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
	2019
Pages	8121-8124
Language	英语
ISSN	2153-6996
ISBN	978-1-5386-9154-0
Abstract	CFOSCAT is the first rotating fan-beam scatterometer in space. It is on board the China France Oceanography Satellite (CFOSAT) launched on 29th October, 2018. It can provide wind field products in the unit of Wind Vector Cells. One of the advantages for this novel antenna geometry is being able to obtain more observations of the scene, though for edge and nadir WVCs, sampling issues as well as larger noise level hinder wind retrievals. This is first analyzed in this paper and a new procedure is proposed, tailored for those observations of CFOSCAT. In the proposed method, wind speeds are retrieved from Neural Networks trained for specific edge and nadir WVCs, and based on the obtained speed, a modified principle component analysis (PCA) is applied for CFOSCAT wind direction retrieval. The new process chain is verified by simulated data, and further research is soon planned modifying it for real data after they are released.
Keyword	CFOSAT Scatterometer edge and nadir WVCs wind field
Conference Name	IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Conference Date	JUL 28-AUG 02, 2019
Conference Place	Yokohama, JAPAN
Indexed By	CPCI
Document Type	会议论文
Identifier	http://ir.nssc.ac.cn/handle/122/7796
Collection	中国科学院国家空间科学中心
Affiliation	1.Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Microwave Remote Sensing, Beijing, Peoples R China 2.Royol Netherlands Meteorol Inst KNMI, De Bilt, Netherlands
Recommended Citation GB/T 7714	Xu, Xingou,Stoffelen, Ad. WIND RETRIEVALFOR CFOSCAT EDGE AND NADIR OBSERVATIONS BASED ON NEURAL NETWORKS AND IMPROVED PRINCIPLE COMPONENT ANALYSIS[C],2019:8121-8124.

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