Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products

doi:10.1016/j.jag.2022.103173

	Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products
	Hu, Chuanmin 1; Qi, Lin 2,3; Hu, Lianbo 4; Cui, Tingwei 5; Xing, Qianguo6 ; He, Mingxia 4; Wang, Ning 7; Xiao, Yanfang 8; Sun, Deyong 9; Lu, Yingcheng 10; Yuan, Chao 8; Wu, Mengquan 11; Wang, Changying 11,12; Chen, Yanlong 12,13; Xu, Haipeng 13,14; Sun, Li 'e 15; Guo, Maohua 15,16; Wang, Menghua 2
发表期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
	2023-02-01
卷号	116 页码:18
关键词	Ulva prolifera Coverage Biomass Remote sensing MODIS VIIRS OLCI GOCI MSI OLI GaoFen CZI FAI AFAI NDVI DVI VB-FAH EVI
DOI	10.1016/j.jag.2022.103173
通讯作者	Hu, Chuanmin(huc@usf.edu)
英文摘要	Since the first report in 2008, macroalgal blooms of Ulva prolifera (often called green tides) in the Yellow Sea have occurred every year, with their origins, transport pathways, temporal changes, as well as causes and consequences studied extensively. Of these studies, satellite remote sensing has been used widely to detect the bloom presence and quantify the bloom size (i.e., U. prolifera coverage in km2 or biomass in kilotons). However, substantial variability has been found in the refereed literature in the remote sensing methodology, results, and interpretation of the U. prolifera coverage, especially in the attempts to study inter-annual changes or long-term trends. There are often inconsistent or contradicting results even from the same satellite sensor. Such in-consistencies or contradictions create difficulty not only within the remote sensing community when presenting new methodology or results, but also to researchers when attempting to use the remote sensing results to make predictions or perform impact assessments. Here, we review the literature on the remote sensing methodology to detect and quantify U. prolifera blooms, and make recommendations based on physical principles. Specifically, we propose the following conceptual guidelines: 1) a reliable index or algorithm should be relatively tolerant to perturbations by non-optimal observing conditions (thick aerosols, thin clouds, moderate sun glint, cloud-adjacent straylight, which can all be found frequently in the study region) for presence/absence detection, as well as to small errors in the selected thresholds to quantify U. prolifera; 2) a reliable index or algorithm should also make it relatively easy to account for variability in subpixel coverage of U. prolifera (i.e., through pixel unmixing) in order to obtain an accurate estimate of total U. prolifera coverage from an image; 3) a reliable data product (i.e., U. prolifera maps) should be able to account for the variable clouds when interpreting spatial patterns or temporal changes, with uncertainty estimates provided whenever possible; and 4) both the algorithm and the data product should minimize manual work in order to make them more objective and repeatable by other researchers. Finally, we show different types of time series of U. prolifera amounts in the Yellow Sea using
资助机构	National Natural Science Foundation of China ; JPSS/NOAA ocean color cal/val program ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Chinese Academy of Sciences
收录类别	SCI
语种	英语
关键词[WOS]	SOUTHERN YELLOW SEA ; LARGEST MACROALGAL BLOOM ; FLOATING-MACROALGAE ; INTERANNUAL VARIABILITY ; SEAWEED AQUACULTURE ; REMOTE ESTIMATION ; ALGAE BLOOMS ; COVERAGE ; EXPANSION ; COASTAL
研究领域[WOS]	Remote Sensing
WOS记录号	WOS:000918428300001
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/32069
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	Hu, Chuanmin
作者单位	1.Univ S Florida, Coll Marine Sci, St Petersburg, FL 33620 USA 2.NOAA Ctr Satellite Applicat & Res, College Pk, MD USA 3.Global Sci & Technol Inc, Greenbelt, MD USA 4.Ocean Univ China, Ocean Remote Sensing Inst, Qingdao, Shandong, Peoples R China 5.Sun Yat Sen Univ, Sch Atmospher Sci, Key Lab Trop Atmosphere Ocean Syst, Southern Marine Sci & Engn Guangdong Lab, Zhuhai, Guangdong, Peoples R China 6.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Shandong, Peoples R China 7.North China Sea Marine Forecasting Ctr State Ocean, Qingdao, Shandong, Peoples R China 8.Minist Nat Resources, Inst Oceanog 1, Qingdao, Shandong, Peoples R China 9.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing, Peoples R China 10.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing, Peoples R China 11.Ludong Univ, Coll Resources & Environm Engn, Yantai, Shandong, Peoples R China 12.Qingdao Univ, Coll Comp Sci & Technol, Qingdao, Peoples R China 13.Natl Marine Environm Monitoring Ctr, Dalian, Peoples R China 14.Lianyungang Sea Area Use & Protect Dynam Managemen, Lianyugang, Jiangsu, Peoples R China 15.Qingdao Ecol & Environm Monitoring Ctr Shandong Pr, Qingdao, Shandong, Peoples R China 16.Natl Satellite Ocean Applicat Serv, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Hu, Chuanmin,Qi, Lin,Hu, Lianbo,et al. Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2023,116:18.
APA	Hu, Chuanmin.,Qi, Lin.,Hu, Lianbo.,Cui, Tingwei.,Xing, Qianguo.,...&Wang, Menghua.(2023).Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,116,18.
MLA	Hu, Chuanmin,et al."Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 116(2023):18.