A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR
Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2269 |
| _version_ | 1827671517051224064 |
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| author | Peng Yu Wenxiang Xu Xiaojing Zhong Johnny A. Johannessen Xiao-Hai Yan Xupu Geng Yuanrong He Wenfang Lu |
| author_facet | Peng Yu Wenxiang Xu Xiaojing Zhong Johnny A. Johannessen Xiao-Hai Yan Xupu Geng Yuanrong He Wenfang Lu |
| author_sort | Peng Yu |
| collection | DOAJ |
| description | Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future. |
| first_indexed | 2024-03-10T03:43:56Z |
| format | Article |
| id | doaj.art-70cfbeee2e424764a1b5c2ca3b3272a6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:43:56Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-70cfbeee2e424764a1b5c2ca3b3272a62023-11-23T09:13:06ZengMDPI AGRemote Sensing2072-42922022-05-01149226910.3390/rs14092269A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SARPeng Yu0Wenxiang Xu1Xiaojing Zhong2Johnny A. Johannessen3Xiao-Hai Yan4Xupu Geng5Yuanrong He6Wenfang Lu7College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaKey Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National Engineering Research Centre of Geo-Spatial Information Technology, Fuzhou University, Fuzhou 350002, ChinaCollege of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, ChinaNansen Environmental and Remote Sensing Center and Geophysical Institute, University of Bergen, N-5006 Bergen, NorwayCenter for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USAFujian Engineering Research Center for Ocean Remote Sensing Big Data, Xiamen University, Xiamen 361005, ChinaCollege of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Marine Sciences, Sun Yat-sen University, Guangzhou 510080, ChinaBased on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future.https://www.mdpi.com/2072-4292/14/9/2269C-band SARsea surface windSentinel-1ASCATneural network |
| spellingShingle | Peng Yu Wenxiang Xu Xiaojing Zhong Johnny A. Johannessen Xiao-Hai Yan Xupu Geng Yuanrong He Wenfang Lu A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR Remote Sensing C-band SAR sea surface wind Sentinel-1 ASCAT neural network |
| title | A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR |
| title_full | A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR |
| title_fullStr | A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR |
| title_full_unstemmed | A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR |
| title_short | A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR |
| title_sort | neural network method for retrieving sea surface wind speed for c band sar |
| topic | C-band SAR sea surface wind Sentinel-1 ASCAT neural network |
| url | https://www.mdpi.com/2072-4292/14/9/2269 |
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