Articles | Volume 12, issue 11
https://doi.org/10.5194/bg-12-3369-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-12-3369-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Jun 2015
Research article |
| 04 Jun 2015
Remote sensing the sea surface CO2 of the Baltic Sea using the SOMLO methodology
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
A. A. Charantonis
Centre d'études et de recherche en informatique, Conservatoire des Arts et Métiers, Paris, France
Laboratoire d'océanographie et du climat: expérimentations et approches numériques, Université Pierre et Marie Curie, Paris, France
A. Rutgerson
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
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23 citations as recorded by crossref.
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- Development of subsurface chlorophyll maximum layer and its contribution to the primary productivity of water column in a large subtropical reservoir H. Miao et al. 10.1016/j.envres.2023.116118
- Subsurface temperature estimation from remote sensing data using a clustering-neural network method W. Lu et al. 10.1016/j.rse.2019.04.009
- From data to insights: Upscaling riverine GHG fluxes in Germany with machine learning R. Mwanake et al. 10.1016/j.scitotenv.2024.177984
- Machine Learning Application in Water Quality Using Satellite Data N. Hassan & C. Woo 10.1088/1755-1315/842/1/012018
- Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA) X. Song et al. 10.3390/rs8070558
- Net Sea–Air CO$_{2}$ Fluxes and Modeled Partial Pressure of CO$_{2}$ in Open Ocean of Bay of Bengal A. Dixit et al. 10.1109/JSTARS.2019.2902253
- Estimating surface pCO2 in the northern Gulf of Mexico: Which remote sensing model to use? S. Chen et al. 10.1016/j.csr.2017.10.013
- Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes W. Lu et al. 10.1029/2021JC017925
- Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea S. Zhang et al. 10.3390/rs13020259
- Seasonal Variability in Chlorophyll and Air-Sea CO2 Flux in the Sri Lanka Dome: Hydrodynamic Implications W. Ma et al. 10.3390/rs14143239
- pCO2 Algorithms for the Baltic Sea Based on Ship, Modelled, and Satellite Data S. Kuzmina & P. Lobanova 10.1134/S0001437024700917
- An offshore subsurface thermal structure inversion method by coupling ensemble learning and tide model for the South Yellow Sea F. Yu et al. 10.3389/fmars.2022.1075938
- Air–sea CO2exchange in the Baltic Sea—A sensitivity analysis of the gas transfer velocity L. Gutiérrez-Loza et al. 10.1016/j.jmarsys.2021.103603
- 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
- A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO2 Using Satellite Oceanographic Data K. Krishna et al. 10.1109/JSTARS.2020.3026363
- Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. 10.5194/bg-20-3919-2023
- Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar Y. Wang et al. 10.1109/TGRS.2024.3379984
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- Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea W. Li et al. 10.3389/fmars.2023.1181095
- The potential of using remote sensing data to estimate air–sea CO2 exchange in the Baltic Sea G. Parard et al. 10.5194/esd-8-1093-2017
- Completion of a Sparse GLIDER Database Using Multi-iterative Self-Organizing Maps (ITCOMP SOM) A. Charantonis et al. 10.1016/j.procs.2015.05.496
19 citations as recorded by crossref.
- The potential of using remote sensing data to estimate air–sea CO2 exchange in the Baltic Sea G. Parard et al. 10.5194/esd-8-1093-2017
- Quantifying the Atmospheric CO2 Forcing Effect on Surface Ocean pCO2 in the North Pacific Subtropical Gyre in the Past Two Decades S. Chen et al. 10.3389/fmars.2021.636881
- Development of subsurface chlorophyll maximum layer and its contribution to the primary productivity of water column in a large subtropical reservoir H. Miao et al. 10.1016/j.envres.2023.116118
- Subsurface temperature estimation from remote sensing data using a clustering-neural network method W. Lu et al. 10.1016/j.rse.2019.04.009
- From data to insights: Upscaling riverine GHG fluxes in Germany with machine learning R. Mwanake et al. 10.1016/j.scitotenv.2024.177984
- Machine Learning Application in Water Quality Using Satellite Data N. Hassan & C. Woo 10.1088/1755-1315/842/1/012018
- Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA) X. Song et al. 10.3390/rs8070558
- Net Sea–Air CO$_{2}$ Fluxes and Modeled Partial Pressure of CO$_{2}$ in Open Ocean of Bay of Bengal A. Dixit et al. 10.1109/JSTARS.2019.2902253
- Estimating surface pCO2 in the northern Gulf of Mexico: Which remote sensing model to use? S. Chen et al. 10.1016/j.csr.2017.10.013
- Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes W. Lu et al. 10.1029/2021JC017925
- Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea S. Zhang et al. 10.3390/rs13020259
- Seasonal Variability in Chlorophyll and Air-Sea CO2 Flux in the Sri Lanka Dome: Hydrodynamic Implications W. Ma et al. 10.3390/rs14143239
- pCO2 Algorithms for the Baltic Sea Based on Ship, Modelled, and Satellite Data S. Kuzmina & P. Lobanova 10.1134/S0001437024700917
- An offshore subsurface thermal structure inversion method by coupling ensemble learning and tide model for the South Yellow Sea F. Yu et al. 10.3389/fmars.2022.1075938
- Air–sea CO2exchange in the Baltic Sea—A sensitivity analysis of the gas transfer velocity L. Gutiérrez-Loza et al. 10.1016/j.jmarsys.2021.103603
- 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
- A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO2 Using Satellite Oceanographic Data K. Krishna et al. 10.1109/JSTARS.2020.3026363
- Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. 10.5194/bg-20-3919-2023
- Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar Y. Wang et al. 10.1109/TGRS.2024.3379984
4 citations as recorded by crossref.
- Using satellite data to estimate partial pressure of CO2 in the Baltic Sea G. Parard et al. 10.1002/2015JG003064
- Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea W. Li et al. 10.3389/fmars.2023.1181095
- The potential of using remote sensing data to estimate air–sea CO2 exchange in the Baltic Sea G. Parard et al. 10.5194/esd-8-1093-2017
- Completion of a Sparse GLIDER Database Using Multi-iterative Self-Organizing Maps (ITCOMP SOM) A. Charantonis et al. 10.1016/j.procs.2015.05.496
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Short summary
In this paper, we used combines two existing methods (i.e. self-organizing maps and multiple linear regression) to estimate the ocean surface partial pressure of CO2 in the Baltic Sea from the remotely sensed sea surface temperature, chlorophyll, coloured dissolved organic matter, net primary production, and
mixed-layer depth. The outputs of this research have a horizontal resolution of 4km and cover the 1998–2011 period. These outputs give a monthly map of the Baltic Sea.
In this paper, we used combines two existing methods (i.e. self-organizing maps and multiple...
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