Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data SCIE SCOPUS

Cited 14 time in WEB OF SCIENCE Cited 11 time in Scopus
Title
Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data
Author(s)
Jang, Eunna; Im, Jungho; Park, Geun-Ha; Park, Young-Gyu
KIOST Author(s)
Park, Young Gyu(박영규)
Publication Year
2017-08
Abstract
The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as a carbon sink, and regional and global carbon cycles. This study estimated the fugacity of carbon dioxide (fCO(2)) over the East Sea located between Korea and Japan. In situ measurements, satellite data and products from the Geostationary Ocean Color Imager (GOCI) and the Hybrid Coordinate Ocean Model (HYCOM) reanalysis data were used through stepwise multi-variate nonlinear regression (MNR) and two machine learning approaches (i.e., support vector regression (SVR) and random forest (RF)). We used five ocean parameters-colored dissolved organic matter (CDOM; <0.3 m(-1)), chlorophyll-a concentration (Chl-a; <21 mg/m(3)), mixed layer depth (MLD; <160 m), sea surface salinity (SSS; 32-35), and sea surface temperature (SST; 8-28 degrees C)-and four band reflectance (Rrs) data (400 nm-565 nm) and their ratios as input parameters to estimate surface seawater fCO(2) (270-430 mu atm). Results show that RF generally performed better than stepwise MNR and SVR. The root mean square error (RMSE) of validation results by RF was 5.49 mu atm (1.7%), while those of stepwise MNR and SVR were 10.59 mu atm (3.2%) and 6.82 mu atm (2.1%), respectively. Ocean parameters (i.e., sea surface salinity (SSS), sea surface temperature (SST), and mixed layer depth (MLD)) appeared to contribute more than the individual bands or band ratios from the satellite data. Spatial and seasonal distributions of monthly fCO(2) produced from the RF model and sea-air CO2 flux were also examined.
ISSN
2072-4292
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/1183
DOI
10.3390/rs9080821
Bibliographic Citation
REMOTE SENSING, v.9, no.8, 2017
Publisher
MDPI
Subject
SURFACE-WATER; ULLEUNG BASIN; CO2 FLUXES; PARTIAL-PRESSURE; RANDOM FOREST; PCO(2); FCO(2); CLASSIFICATION; ALGORITHMS; SYSTEM
Keywords
fugacity of CO2; GOCI; HYCOM; machine learning; multi-variate nonlinear regression
Type
Article
Language
English
Document Type
Article
Publisher
MDPI
Related Researcher
Research Interests

ocean circulation,ocean mixing,marine debris,해양순환,해양혼합,해양부유물

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