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- Publisher Website: 10.1109/MGRS.2024.3364678
- Scopus: eid_2-s2.0-85210125148
- WOS: WOS:001303384800001
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Article: Remote Sensing of Ulva Prolifera Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects
| Title | Remote Sensing of Ulva Prolifera Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects |
|---|---|
| Authors | Pan, XinliangCao, MengmengZheng, LongxiaoXiao, YanfangQi, LinXing, QianguoKim, KeunyongSun, DeyongWang, NingGuo, MaohuaWu, MengquanLi, XuyanYuan, ChaoQing, SongQiu, ZhongfengLu, YingchengWang, ChangyingRen, PengCai, XiaoqingSun, LieBao, YuhaiGao, SongWang, ZonglingLiu, RongjieRyu, Joo HyungWang, MengqiuHu, LianboLi, XiaofengCui, Tingwei |
| Issue Date | 2024 |
| Citation | IEEE Geoscience and Remote Sensing Magazine, 2024, v. 12, n. 4, p. 110-131 How to Cite? |
| Abstract | As a marine ecological disaster caused by the explosive proliferation of green macroalgae, green tide impairs economic development and ecological environment, affecting dozens of regions worldwide. The largest green tide in the world occurred in the Yellow Sea, with Ulva prolifera (U. prolifera) as the dominant species. Satellite remote sensing technology, with its advantages of large-scale, long-Time series, and traceability, plays a significant role in U. prolifera monitoring, providing important support for obtaining deeper scientific understanding and promoting disaster prevention and control. To systematically and comprehensively summarize the research progress and identify weaknesses and priorities for future development, this study has reviewed over 350 articles on 'U. prolifera green tide remote sensing in the Yellow Sea' published before November 2023 from three aspects: remote sensing mechanisms (electromagnetic scattering and remote sensing image features), methods (detection, coverage area retrieval, species discrimination, biomass estimation, drift velocity determination, etc.), and applications (growth and decay, interannual variabilities, etc.). Additionally, the challenges, opportunities, and development priorities are analyzed. The findings in this study will promote the future development of U. prolifera remote sensing technology to assist disaster prevention and ecosystem protection. |
| Persistent Identifier | http://hdl.handle.net/10722/355982 |
| ISSN | 2023 Impact Factor: 16.2 2023 SCImago Journal Rankings: 3.118 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Pan, Xinliang | - |
| dc.contributor.author | Cao, Mengmeng | - |
| dc.contributor.author | Zheng, Longxiao | - |
| dc.contributor.author | Xiao, Yanfang | - |
| dc.contributor.author | Qi, Lin | - |
| dc.contributor.author | Xing, Qianguo | - |
| dc.contributor.author | Kim, Keunyong | - |
| dc.contributor.author | Sun, Deyong | - |
| dc.contributor.author | Wang, Ning | - |
| dc.contributor.author | Guo, Maohua | - |
| dc.contributor.author | Wu, Mengquan | - |
| dc.contributor.author | Li, Xuyan | - |
| dc.contributor.author | Yuan, Chao | - |
| dc.contributor.author | Qing, Song | - |
| dc.contributor.author | Qiu, Zhongfeng | - |
| dc.contributor.author | Lu, Yingcheng | - |
| dc.contributor.author | Wang, Changying | - |
| dc.contributor.author | Ren, Peng | - |
| dc.contributor.author | Cai, Xiaoqing | - |
| dc.contributor.author | Sun, Lie | - |
| dc.contributor.author | Bao, Yuhai | - |
| dc.contributor.author | Gao, Song | - |
| dc.contributor.author | Wang, Zongling | - |
| dc.contributor.author | Liu, Rongjie | - |
| dc.contributor.author | Ryu, Joo Hyung | - |
| dc.contributor.author | Wang, Mengqiu | - |
| dc.contributor.author | Hu, Lianbo | - |
| dc.contributor.author | Li, Xiaofeng | - |
| dc.contributor.author | Cui, Tingwei | - |
| dc.date.accessioned | 2025-05-19T05:47:04Z | - |
| dc.date.available | 2025-05-19T05:47:04Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.citation | IEEE Geoscience and Remote Sensing Magazine, 2024, v. 12, n. 4, p. 110-131 | - |
| dc.identifier.issn | 2473-2397 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/355982 | - |
| dc.description.abstract | As a marine ecological disaster caused by the explosive proliferation of green macroalgae, green tide impairs economic development and ecological environment, affecting dozens of regions worldwide. The largest green tide in the world occurred in the Yellow Sea, with Ulva prolifera (U. prolifera) as the dominant species. Satellite remote sensing technology, with its advantages of large-scale, long-Time series, and traceability, plays a significant role in U. prolifera monitoring, providing important support for obtaining deeper scientific understanding and promoting disaster prevention and control. To systematically and comprehensively summarize the research progress and identify weaknesses and priorities for future development, this study has reviewed over 350 articles on 'U. prolifera green tide remote sensing in the Yellow Sea' published before November 2023 from three aspects: remote sensing mechanisms (electromagnetic scattering and remote sensing image features), methods (detection, coverage area retrieval, species discrimination, biomass estimation, drift velocity determination, etc.), and applications (growth and decay, interannual variabilities, etc.). Additionally, the challenges, opportunities, and development priorities are analyzed. The findings in this study will promote the future development of U. prolifera remote sensing technology to assist disaster prevention and ecosystem protection. | - |
| dc.language | eng | - |
| dc.relation.ispartof | IEEE Geoscience and Remote Sensing Magazine | - |
| dc.title | Remote Sensing of Ulva Prolifera Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects | - |
| dc.type | Article | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1109/MGRS.2024.3364678 | - |
| dc.identifier.scopus | eid_2-s2.0-85210125148 | - |
| dc.identifier.volume | 12 | - |
| dc.identifier.issue | 4 | - |
| dc.identifier.spage | 110 | - |
| dc.identifier.epage | 131 | - |
| dc.identifier.eissn | 2168-6831 | - |
| dc.identifier.isi | WOS:001303384800001 | - |
