Articles | Volume 18, issue 1
https://doi.org/10.5194/amt-18-211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2
Diego Santaren
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Janne Hakkarainen
Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
Gerrit Kuhlmann
Laboratory for Air Pollution/Environmental Technology, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Erik Koene
Laboratory for Air Pollution/Environmental Technology, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Iolanda Ialongo
Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
Hannakaisa Lindqvist
Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
Janne Nurmela
Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
Johanna Tamminen
Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
Laia Amorós
Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland
Dominik Brunner
Laboratory for Air Pollution/Environmental Technology, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
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Cited
9 citations as recorded by crossref.
- Impact of atmospheric turbulence on the accuracy of point source emission estimates using satellite imagery M. Gałkowski et al. https://doi.org/10.5194/acp-25-13831-2025
- Temporal variability of NOx emissions from power plants: a comparison of satellite- and inventory-based estimates G. Kuhlmann et al. https://doi.org/10.5194/acp-26-4405-2026
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- The Italian effort toward a coordinated observation of Essential Variables (EOV, ECV, EBV) in European Marine Environments. Long-term observation in the central Mediterranean seas and Italian coasts S. Toller et al. https://doi.org/10.3389/fmars.2026.1771479
- Advances in Satellite-Based Monitoring of Urban Emission Sources and Air Quality: A Review S. Naderi et al. https://doi.org/10.1007/s11270-025-09009-4
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
- Deriving regional and point source nitrogen oxides emissions in China from TROPOMI using the directional derivative approach with nonlinear chemical lifetime fitting L. Chen et al. https://doi.org/10.5194/essd-18-2749-2026
- Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations J. Hakkarainen et al. https://doi.org/10.1016/j.rse.2025.114623
- Local water-heat-carbon impacts of forest changes based on satellite observations and statistical modeling in two forest cities of the hilly areas in southern China Z. Zuo et al. https://doi.org/10.1080/10095020.2025.2610567
9 citations as recorded by crossref.
- Impact of atmospheric turbulence on the accuracy of point source emission estimates using satellite imagery M. Gałkowski et al. https://doi.org/10.5194/acp-25-13831-2025
- Temporal variability of NOx emissions from power plants: a comparison of satellite- and inventory-based estimates G. Kuhlmann et al. https://doi.org/10.5194/acp-26-4405-2026
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- The Italian effort toward a coordinated observation of Essential Variables (EOV, ECV, EBV) in European Marine Environments. Long-term observation in the central Mediterranean seas and Italian coasts S. Toller et al. https://doi.org/10.3389/fmars.2026.1771479
- Advances in Satellite-Based Monitoring of Urban Emission Sources and Air Quality: A Review S. Naderi et al. https://doi.org/10.1007/s11270-025-09009-4
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
- Deriving regional and point source nitrogen oxides emissions in China from TROPOMI using the directional derivative approach with nonlinear chemical lifetime fitting L. Chen et al. https://doi.org/10.5194/essd-18-2749-2026
- Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations J. Hakkarainen et al. https://doi.org/10.1016/j.rse.2025.114623
- Local water-heat-carbon impacts of forest changes based on satellite observations and statistical modeling in two forest cities of the hilly areas in southern China Z. Zuo et al. https://doi.org/10.1080/10095020.2025.2610567
Saved (final revised paper)
Latest update: 30 May 2026
Short summary
This study evaluates data-driven inversion methods for estimating CO2 emissions from local sources, such as power plants and cities, using meteorological data and XCO2 and NO2 satellite images rather than atmospheric transport modeling. We assess and compare the performance of five different methods using simulations of 1 year of satellite images, taken from the upcoming Copernicus CO2 Monitoring Mission, covering 15 power plants and the city of Berlin, Germany.
This study evaluates data-driven inversion methods for estimating CO2 emissions from local...