Articles | Volume 17, issue 3
https://doi.org/10.5194/amt-17-1145-2024
© Author(s) 2024. 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-17-1145-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Feb 2024
Research article |
| 16 Feb 2024
| 16 Feb 2024
A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images
Blanca Fuentes Andrade
CORRESPONDING AUTHOR
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
Michael Buchwitz
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
Maximilian Reuter
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
Heinrich Bovensmann
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
Andreas Richter
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
Hartmut Boesch
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
John P. Burrows
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
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13 citations as recorded by crossref.
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- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
- Identification and quantification of CH4 emissions from Madrid landfills using airborne imaging spectrometry and greenhouse gas lidar S. Krautwurst et al. https://doi.org/10.5194/acp-25-14669-2025
- The Downscaling Prediction Algorithm of Traffic Source Carbon Emissions Based on Multisource Remote Sensing Data and Deep Learning L. Zheng et al. https://doi.org/10.1109/TGRS.2025.3628964
- Spatio-Temporal Analysis of CO2 Emissions from Vehicles in Urban Areas: A Satellite Imagery Approach N. Yaacob et al. https://doi.org/10.3390/su162310765
- Monitoring and Forecasting XCO2 Using OCO-2 Satellite Data and Deep Learning K. Lee & K. Kim https://doi.org/10.5572/KOSAE.2024.40.5.572
- 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
- Satellite data-driven estimates of NO x and co-emitted CO2 from coal-fired power plants in China (2021–2024) L. Chen et al. https://doi.org/10.1088/1748-9326/ae84e9
- Robust Spatio-temporal reconstruction of XCO₂ using OCO-2 and OCO-3 satellite observations and deep learning-based uncertainty quantification M. Awad & S. Homayouni https://doi.org/10.1016/j.nexres.2026.101707
- Refining Spatial and Temporal XCO2 Characteristics Observed by Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3 Using Sentinel-5P Tropospheric Monitoring Instrument NO2 Observations in China K. Guo et al. https://doi.org/10.3390/rs16132456
- Satellite-Based Estimation of Urban CO2 Emissions in Shandong Province, China, Using TROPOMI NO2 Observations and Differential Evolution Algorithm Y. Xie et al. https://doi.org/10.3390/rs18101470
- A study of measurement scenarios for the future CO2M mission: avoidance of detector saturation and the impact on XCO2 retrievals M. Weimer et al. https://doi.org/10.5194/amt-18-3321-2025
- Evaluation of coal mine methane inventory methods using aircraft-based approaches in the Bowen Basin, Australia S. Harris et al. https://doi.org/10.5194/acp-26-10043-2026
13 citations as recorded by crossref.
- CuCl/Ph3P-catalyzed multicomponent polymerization of CO2 to prepare functional poly(alkynoate)s and fused heterocyclic polymers under atmospheric pressure and near ambient temperature T. Duan et al. https://doi.org/10.1088/2752-5724/adfd76
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
- Identification and quantification of CH4 emissions from Madrid landfills using airborne imaging spectrometry and greenhouse gas lidar S. Krautwurst et al. https://doi.org/10.5194/acp-25-14669-2025
- The Downscaling Prediction Algorithm of Traffic Source Carbon Emissions Based on Multisource Remote Sensing Data and Deep Learning L. Zheng et al. https://doi.org/10.1109/TGRS.2025.3628964
- Spatio-Temporal Analysis of CO2 Emissions from Vehicles in Urban Areas: A Satellite Imagery Approach N. Yaacob et al. https://doi.org/10.3390/su162310765
- Monitoring and Forecasting XCO2 Using OCO-2 Satellite Data and Deep Learning K. Lee & K. Kim https://doi.org/10.5572/KOSAE.2024.40.5.572
- 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
- Satellite data-driven estimates of NO x and co-emitted CO2 from coal-fired power plants in China (2021–2024) L. Chen et al. https://doi.org/10.1088/1748-9326/ae84e9
- Robust Spatio-temporal reconstruction of XCO₂ using OCO-2 and OCO-3 satellite observations and deep learning-based uncertainty quantification M. Awad & S. Homayouni https://doi.org/10.1016/j.nexres.2026.101707
- Refining Spatial and Temporal XCO2 Characteristics Observed by Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3 Using Sentinel-5P Tropospheric Monitoring Instrument NO2 Observations in China K. Guo et al. https://doi.org/10.3390/rs16132456
- Satellite-Based Estimation of Urban CO2 Emissions in Shandong Province, China, Using TROPOMI NO2 Observations and Differential Evolution Algorithm Y. Xie et al. https://doi.org/10.3390/rs18101470
- A study of measurement scenarios for the future CO2M mission: avoidance of detector saturation and the impact on XCO2 retrievals M. Weimer et al. https://doi.org/10.5194/amt-18-3321-2025
- Evaluation of coal mine methane inventory methods using aircraft-based approaches in the Bowen Basin, Australia S. Harris et al. https://doi.org/10.5194/acp-26-10043-2026
Saved (final revised paper)
Latest update: 05 Aug 2026
Short summary
We developed a method to estimate CO2 emissions from localized sources, such as power plants, using satellite data and applied it to estimate CO2 emissions from the Bełchatów Power Station (Poland). As the detection of CO2 emission plumes from satellite data is difficult, we used observations of co-emitted NO2 to constrain the emission plume region. Our results agree with CO2 emission estimations based on the power-plant-generated power and emission factors.
We developed a method to estimate CO2 emissions from localized sources, such as power plants,...
