Assessment of Satellite-Based Chlorophyll-a Algorithms in Eutrophic Korean Coastal Waters: Jinhae Bay Case Study SCIE SCOPUS

Cited 25 time in WEB OF SCIENCE Cited 27 time in Scopus
Title
Assessment of Satellite-Based Chlorophyll-a Algorithms in Eutrophic Korean Coastal Waters: Jinhae Bay Case Study
Author(s)
Yoon, Joo-Eun; Lim, Jae-Hyun; Son, SeungHyun; Youn, Seok-Hyun; Oh, Hyun-Ju; Hwang, Jae-Dong; Kwon, Jae-Il; Kim, Seong-Su; Kim, Il-Nam
KIOST Author(s)
Kwon, Jae Il(권재일)
Alternative Author(s)
권재일
Publication Year
2019-06
Abstract
Jinhae Bay, one of the most important aquaculture areas in Korean coastal waters, has suffered from serious environmental problems due to intensive anthropogenic activities since the 1970s. Determining the response of coastal ecosystems in Korea to anthropogenic activities requires understanding the characteristics of chlorophyll-a concentration (Chl-a), and the high spatiotemporal resolution of the Geostationary Ocean Color Imager (GOCI) can aid these efforts. However, producing reliable satellite-based Chl-a estimates is challenging in optically complex coastal waters and the Chl-a estimation algorithms must be assessed regionally. Based on in situ Chl-a measurements collected in Jinhae Bay between 2011 and 2016, we evaluated GOCI-derived Chl-a estimates obtained using six ocean color Chl-a algorithms: two standard open ocean algorithms, one GOCI-standard algorithm, and three Tassan's algorithms regionally modified for Korean waters. All of the algorithms tended to underestimate high Chl-a values >0.9 mg m(-3). The Yellow Sea Large Marine Ecosystem Ocean Color Project (YOC) algorithm, one of the modified Tassan's algorithms, provided the best fit to the in situ Chl-a measurements in Jinhae Bay (r = 0.51, p < 0.05), including appropriate representations of the spatial and temporal variation. Therefore, this algorithm can be considered a baseline approach for satellite-based long-term coastal monitoring systems in Jinhae Bay.
ISSN
2296-7745
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/621
DOI
10.3389/fmars.2019.00359
Bibliographic Citation
FRONTIERS IN MARINE SCIENCE, v.6, 2019
Publisher
FRONTIERS MEDIA SA
Keywords
ocean color algorithm; coastal ecosystem; geostationary ocean color imager (GOCI); Korean waters; remote sensing
Type
Article
Language
English
Document Type
Article
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