Remote Sensing of <i>Ulva Prolifera</i> Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects

doi:10.1109/MGRS.2024.3364678

	*Remote Sensing of Ulva Prolifera* Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects**
	Pan, Xinliang 1,2,3; Cao, Mengmeng 4; Zheng, Longxiao 1,2,3; Xiao, Yanfang 5; Qi, Lin 6; Xing, Qianguo7 ; Kim, Keunyong 8; Sun, Deyong 9; Wang, Ning10 ; Guo, Maohua 11; Wu, Mengquan 12; Li, Xuyan 12; Yuan, Chao 13; Qing, Song 14; Qiu, Zhongfeng 15; Lu, Yingcheng 11; Wang, Changying 12; Ren, Peng 13; Cai, Xiaoqing 10; Sun, Lie 14; Bao, Yuhai ; Gao, Song ; Wang, Zongling ; Liu, Rongjie 5; Ryu, Joo-Hyung 8; Wang, Mengqiu 15; Hu, Lianbo 16; Li, Xiaofeng 17; Cui, Tingwei 1,2,3
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
ISSN	2473-2397
	2024-08-28
页码	22
关键词	Remote sensing Monitoring Green products Optical sensors Tides Synthetic aperture radar Satellites
DOI	10.1109/MGRS.2024.3364678
通讯作者	Cui, Tingwei([email protected])
英文摘要	As a marine ecological disaster caused by the explosive proliferation of green macroalgae, green tides impair economic development and the ecological environment, affecting dozens of regions worldwide. The largest green tide in the world occurs in the Yellow Sea, with Ulva prolifera (U. prolifera) the dominant species. Satellite remote sensing technology, with its advantages of a large scale, a 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 research progress and identify weaknesses and priorities for future development, this article reviews 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, and so on), and applications (growth and decay, interannual variabilities, and so forth). Additionally, challenges, opportunities, and development priorities are analyzed (see "Article Contents"). The findings in this article promote the future development of U. prolifera remote sensing technology to assist with disaster prevention and ecosystem protection.
资助机构	Southern Marine Science and Engineering Guangdong Laboratory (Zhu-hai) ; National Key Research and Development Program of China ; Fundamental Research Funds for the Central Universities ; Sun Yat-sen University ; China-Korea Joint OceanSun Research Center, China
收录类别	SCI
语种	英语
关键词[WOS]	LARGEST MACROALGAL BLOOM ; SEAWEED AQUACULTURE ; CHINA ; ALGAE ; BIOMASS ; EXTRACTION ; COVERAGE ; IMAGER ; THRESHOLD ; EXPANSION
研究领域[WOS]	Geochemistry & Geophysics ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001303384800001
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/35813
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者	Cui, Tingwei
作者单位	1.Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai 519082, Peoples R China 2.Minist Educ, Key Lab Trop Atmosphere Ocean Syst, Zhuhai 519082, Peoples R China 3.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 4.Inner Mongolia Normal Univ, Coll Geog Sci, Hohhot 010022, Peoples R China 5.Minist Nat Resources, Inst Oceanog 1, Qingdao, Peoples R China 6.NOAA, Ctr Satellite Applicat & Res, College Pk, MD 20740 USA 7.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 8.Korea Inst Ocean Sci & Technol, Korea Ocean Satellite Ctr, Busan 49111, South Korea 9.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China 10.Minist Nat Resources, North China Sea Marine Forecasting Ctr, Qingdao 266061, Peoples R China 11.Minist Nat Resources, Natl Satellite Ocean Applicat Serv, Beijing 100081, Peoples R China 12.Ludong Univ, Coll Resources & Environm Engn, Yantai 264025, Peoples R China 13.China Univ Petr East China, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China 14.Qingdao Ecol & Environm Monitoring Ctr Shandong Pr, Qingdao 266003, Peoples R China 15.Wuhan Univ, Sch Remote Sensing Informat Engn, Wuhan 430079, Peoples R China 16.Ocean Univ China, Ocean Remote Sensing Inst, Qingdao 266100, Peoples R China 17.Chinese Acad Sci, Big Data Ctr, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
推荐引用方式 GB/T 7714	Pan, Xinliang,Cao, Mengmeng,Zheng, Longxiao,et al. Remote Sensing of Ulva Prolifera Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects[J]. IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE,2024:22.
APA	Pan, Xinliang.,Cao, Mengmeng.,Zheng, Longxiao.,Xiao, Yanfang.,Qi, Lin.,...&Cui, Tingwei.(2024).Remote Sensing of Ulva Prolifera Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects.IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE,22.
MLA	Pan, Xinliang,et al."Remote Sensing of Ulva Prolifera Green Tide in the Yellow Sea Using Multisource Satellite Data: Progress and prospects".IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2024):22.