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

doi:10.1016/j.jag.2022.103173

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室

	Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products
	Hu, Chuanmin 16; Qi, Lin 14,15; Hu, Lianbo 13; Cui, Tingwei 12; Xing, Qianguo 11; He, Mingxia 13; Wang, Ning 10; Xiao, Yanfang 9; Sun, Deyong 8; Lu, Yingcheng 7
刊名	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
	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
ISSN号	1569-8432
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
WOS关键词	SOUTHERN YELLOW SEA ; LARGEST MACROALGAL BLOOM ; FLOATING-MACROALGAE ; INTERANNUAL VARIABILITY ; SEAWEED AQUACULTURE ; REMOTE ESTIMATION ; ALGAE BLOOMS ; COVERAGE ; EXPANSION ; COASTAL
WOS研究方向	Remote Sensing
语种	英语
WOS记录号	WOS:000918428300001
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
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/32068]
专题	烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
通讯作者	Hu, Chuanmin
作者单位	1.Natl Satellite Ocean Applicat Serv, Beijing, Peoples R China 2.Qingdao Ecol & Environm Monitoring Ctr Shandong Pr, Qingdao, Shandong, Peoples R China 3.Lianyungang Sea Area Use & Protect Dynam Managemen, Lianyugang, Jiangsu, Peoples R China 4.Natl Marine Environm Monitoring Ctr, Dalian, Peoples R China 5.Qingdao Univ, Coll Comp Sci & Technol, Qingdao, Peoples R China 6.Ludong Univ, Coll Resources & Environm Engn, Yantai, Shandong, Peoples R China 7.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing, Peoples R China 8.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing, Peoples R China 9.Minist Nat Resources, Inst Oceanog 1, Qingdao, Shandong, Peoples R China 10.North China Sea Marine Forecasting Ctr State Ocean, Qingdao, Shandong, 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.