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Remote Sens. 2017, 9(8), 821; https://doi.org/10.3390/rs9080821

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

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
2
East Sea Research Institute, Korea Institute of Ocean Science and Technology, Uljin 36315, Korea
3
Korea Institute of Ocean Science and Technology, Ansan 15627, Korea
*
Author to whom correspondence should be addressed.
Received: 24 May 2017 / Revised: 1 August 2017 / Accepted: 8 August 2017 / Published: 10 August 2017
(This article belongs to the Section Ocean Remote Sensing)
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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 (ƒCO2) 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/m3), mixed layer depth (MLD; <160 m), sea surface salinity (SSS; 32–35), and sea surface temperature (SST; 8–28 °C)—and four band reflectance (Rrs) data (400 nm–565 nm) and their ratios as input parameters to estimate surface seawater ƒCO2 (270–430 μatm). 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 μatm (1.7%), while those of stepwise MNR and SVR were 10.59 μatm (3.2%) and 6.82 μatm (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 ƒCO2 produced from the RF model and sea-air CO2 flux were also examined. View Full-Text
Keywords: fugacity of CO2; GOCI; HYCOM; machine learning; multi-variate nonlinear regression fugacity of CO2; GOCI; HYCOM; machine learning; multi-variate nonlinear regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Jang, E.; Im, J.; Park, G.-H.; Park, Y.-G. Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data. Remote Sens. 2017, 9, 821.

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