Estimation of surface seawater fugacity of carbon dioxide using satellite data and machine learning

Title
Estimation of surface seawater fugacity of carbon dioxide using satellite data and machine learning
Author(s)
장은나; 임정호; 박근하; 박영규
KIOST Author(s)
Park, Young Gyu(박영규)
Publication Year
2016-12-13
Abstract
The ocean controls the climate of Earth by absorbing and releasing CO2 through the carbon cycle. The amount of CO2 in the ocean has increased since the industrial revolution. High CO2 concentration in the ocean has a negative influence to maring organisims and reduces the ability of absorbing CO2 in the ocean. This study estimated surface seawater fugacity of CO2 (fCO2) in the East Sea of Korea using Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and Hybrid Coordinate Ocean Model (HYCOM) reanalysis data. GOCI is the world first geostationary ocean color observation satellite sensor, and it provides 8 images with 8 bands hourly per day from 9 am to 4 pm at 500m resolution. Two machine learning approaches (i.e., random forest and support vector regression) were used to model fC02 in this study. While most of the existing studies used multiple linear regression to estimate the pressure of CO2 in the ocean, machine learning may handle more complex relationship between surface seawater fC02 and ocean parameters in a dynamic spatiotemporal environment. Five ocean related parameters, colored dissolved organic matter (CDOM), chlorophyll-a (chla), sea surface temperature (SST), sea surface salinity (SSS), and mixed layer depth (MLD), were used as input variables. This study examined two schemes, one with GOCl-derived products and the other with MODIS-derived oaring organisims and reduces the ability of absorbing CO2 in the ocean. This study estimated surface seawater fugacity of CO2 (fCO2) in the East Sea of Korea using Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and Hybrid Coordinate Ocean Model (HYCOM) reanalysis data. GOCI is the world first geostationary ocean color observation satellite sensor, and it provides 8 images with 8 bands hourly per day from 9 am to 4 pm at 500m resolution. Two machine learning approaches (i.e., random forest and support vector regression) were used to model fC02 in this study. While most of the existing studies used multiple linear regression to estimate the pressure of CO2 in the ocean, machine learning may handle more complex relationship between surface seawater fC02 and ocean parameters in a dynamic spatiotemporal environment. Five ocean related parameters, colored dissolved organic matter (CDOM), chlorophyll-a (chla), sea surface temperature (SST), sea surface salinity (SSS), and mixed layer depth (MLD), were used as input variables. This study examined two schemes, one with GOCl-derived products and the other with MODIS-derived o
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/24256
Bibliographic Citation
2016 AGU Fall Meeting, 2016
Publisher
American Geophysical Union
Type
Conference
Language
English
Publisher
American Geophysical Union
Related Researcher
Research Interests

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

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