Articles | Volume 13, issue 2
https://doi.org/10.5194/os-13-303-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-13-303-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note
| 
19 Apr 2017
Technical note |
| 19 Apr 2017
Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping
Jiye Zeng
CORRESPONDING AUTHOR
Centre for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
Tsuneo Matsunaga
Centre for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
Nobuko Saigusa
Centre for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
Tomoko Shirai
Centre for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
Shin-ichiro Nakaoka
Centre for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
Zheng-Hong Tan
Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, China
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Cited
14 citations as recorded by crossref.
- Variations in the summer oceanic pCO2 and carbon sink in Prydz Bay using the self-organizing map analysis approach S. Xu et al. 10.5194/bg-16-797-2019
- A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
- A Surface Ocean CO2 Reference Network, SOCONET and Associated Marine Boundary Layer CO2 Measurements R. Wanninkhof et al. 10.3389/fmars.2019.00400
- Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis Z. Wang et al. 10.5194/essd-15-1711-2023
- A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. 10.3390/rs17101776
- The impact of the South-East Madagascar Bloom on the oceanic CO2 sink N. Metzl et al. 10.5194/bg-19-1451-2022
- Performance Evaluation of Gradient Descent Optimizers in Estuarine Turbidity Estimation with Multilayer Perceptron and Sentinel-2 Imagery N. Ndou & N. Nontongana 10.3390/hydrology11100164
- Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance Y. Wang et al. 10.1016/j.scitotenv.2025.179856
- Reconstruction of global surface ocean pCO2 using region-specific predictors based on a stepwise FFNN regression algorithm G. Zhong et al. 10.5194/bg-19-845-2022
- Modeling and Prediction of Environmental Factors and Chlorophyll a Abundance by Machine Learning Based on Tara Oceans Data Z. Cui et al. 10.3390/jmse10111749
- High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning C. Galdies & R. Guerra 10.3390/w15081454
- Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. 10.1021/acsomega.2c06441
- Generalization of Parameter Selection of SVM and LS-SVM for Regression J. Zeng et al. 10.3390/make1020043
- Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data E. Jang et al. 10.3390/rs9080821
14 citations as recorded by crossref.
- Variations in the summer oceanic pCO2 and carbon sink in Prydz Bay using the self-organizing map analysis approach S. Xu et al. 10.5194/bg-16-797-2019
- A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
- A Surface Ocean CO2 Reference Network, SOCONET and Associated Marine Boundary Layer CO2 Measurements R. Wanninkhof et al. 10.3389/fmars.2019.00400
- Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis Z. Wang et al. 10.5194/essd-15-1711-2023
- A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. 10.3390/rs17101776
- The impact of the South-East Madagascar Bloom on the oceanic CO2 sink N. Metzl et al. 10.5194/bg-19-1451-2022
- Performance Evaluation of Gradient Descent Optimizers in Estuarine Turbidity Estimation with Multilayer Perceptron and Sentinel-2 Imagery N. Ndou & N. Nontongana 10.3390/hydrology11100164
- Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance Y. Wang et al. 10.1016/j.scitotenv.2025.179856
- Reconstruction of global surface ocean pCO2 using region-specific predictors based on a stepwise FFNN regression algorithm G. Zhong et al. 10.5194/bg-19-845-2022
- Modeling and Prediction of Environmental Factors and Chlorophyll a Abundance by Machine Learning Based on Tara Oceans Data Z. Cui et al. 10.3390/jmse10111749
- High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning C. Galdies & R. Guerra 10.3390/w15081454
- Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. 10.1021/acsomega.2c06441
- Generalization of Parameter Selection of SVM and LS-SVM for Regression J. Zeng et al. 10.3390/make1020043
- Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data E. Jang et al. 10.3390/rs9080821
Latest update: 29 Aug 2025
Short summary
Three machine learning models were investigated for the reconstruction of global surface ocean CO2 concentration. They include self-organizing maps (SOMs), feedforward neural networks (FNNs), and support vector machines (SVMs). Our results show that the SVM performs the best, the FNN the second, and the SOM the worst. While the SOM does not have over-fitting problems, it is sensitive to data scaling and its discrete interpolation may not be good for some applications.
Three machine learning models were investigated for the reconstruction of global surface ocean...
