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Estimation of the Particulate Organic Carbon to Chlorophyll-a Ratio Using MODIS-Aqua in the East/Japan Sea, South Korea

Authors :
Dabin Lee
SeungHyun Son
HuiTae Joo
Kwanwoo Kim
Myung Joon Kim
Hyo Keun Jang
Mi Sun Yun
Chang-Keun Kang
Sang Heon Lee
Source :
Remote Sensing, Vol 12, Iss 5, p 840 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

In recent years, the change of marine environment due to climate change and declining primary productivity have been big concerns in the East/Japan Sea, Korea. However, the main causes for the recent changes are still not revealed clearly. The particulate organic carbon (POC) to chlorophyll-a (chl-a) ratio (POC:chl-a) could be a useful indicator for ecological and physiological conditions of phytoplankton communities and thus help us to understand the recent reduction of primary productivity in the East/Japan Sea. To derive the POC in the East/Japan Sea from a satellite dataset, the new regional POC algorithm was empirically derived with in-situ measured POC concentrations. A strong positive linear relationship (R2 = 0.6579) was observed between the estimated and in-situ measured POC concentrations. Our new POC algorithm proved a better performance in the East/Japan Sea compared to the previous one for the global ocean. Based on the new algorithm, long-term POC:chl-a ratios were obtained in the entire East/Japan Sea from 2003 to 2018. The POC:chl-a showed a strong seasonal variability in the East/Japan Sea. The spring and fall blooms of phytoplankton mainly driven by the growth of large diatoms seem to be a major factor for the seasonal variability in the POC:chl-a. Our new regional POC algorithm modified for the East/Japan Sea could potentially contribute to long-term monitoring for the climate-associated ecosystem changes in the East/Japan Sea. Although the new regional POC algorithm shows a good correspondence with in-situ observed POC concentrations, the algorithm should be further improved with continuous field surveys.

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.5b696abb939f418a9c2fc6056d934b1a
Document Type :
article
Full Text :
https://doi.org/10.3390/rs12050840