Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14577-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-14577-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Nov 2023
Research article |
| 24 Nov 2023
| 24 Nov 2023
Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants
Carbon Mapper, Pasadena, CA, USA
Arizona Institute for Resilience, University of Arizona, Tucson, AZ, USA
Andrew K. Thorpe
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Charles E. Miller
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Alana K. Ayasse
Carbon Mapper, Pasadena, CA, USA
Ralph Jiorle
Carbon Mapper, Pasadena, CA, USA
Riley M. Duren
Carbon Mapper, Pasadena, CA, USA
Arizona Institute for Resilience, University of Arizona, Tucson, AZ, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Ray Nassar
Climate Research Division, Environment and Climate Change Canada, Toronto, ON, Canada
Jon-Paul Mastrogiacomo
Department of Physics, University of Toronto, Toronto, ON, Canada
Robert R. Nelson
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
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Cited
21 citations as recorded by crossref.
- Quantifying CO2 emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1 G. Han et al. https://doi.org/10.1016/j.rse.2024.114368
- 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
- High-resolution observations of NO2 and CO2 emission plumes from EnMAP satellite measurements C. Borger et al. https://doi.org/10.1088/1748-9326/adc0b1
- Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning H. Couture et al. https://doi.org/10.3390/rs16071290
- The Total Emission Estimation of Thermal Power Plants Using a Top-Down Approach Strongly Impacted by Satellite Spatial Resolution, Precision, and Monitoring Frequency X. Li et al. https://doi.org/10.34133/remotesensing.0469
- Achieving net-zero carbon in the pulp and paper industry: Tackling scope 1, 2, and 3 emission hotspots N. Barrios et al. https://doi.org/10.1016/j.rcradv.2026.200318
- Facility-Scale Detection and Quantification of Gas Flaring Using Imaging Spectrometers J. Kim et al. https://doi.org/10.1021/acsestair.6c00181
- Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches C. He et al. https://doi.org/10.1016/j.rse.2024.114192
- Global gridded NOx emissions using TROPOMI observations A. Rey-Pommier et al. https://doi.org/10.5194/essd-17-3329-2025
- 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
- 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
- Real-time monitoring of daily carbon emissions and electricity generation from fossil fuel power plants using geostationary satellite band data and deep learning techniques H. Mo et al. https://doi.org/10.1016/j.energy.2025.137570
- Facility-Scale CO2 Emission Quantification for Coal-Fired Power Plants Using Multisource Hyperspectral Satellite Observations L. Qi et al. https://doi.org/10.1109/LGRS.2026.3709724
- 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
- HyperGas 1.0: a python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantification X. Zhang et al. https://doi.org/10.5194/gmd-19-5979-2026
- Advanced satellite-based model for precise measurement of point source CO 2 emission Y. Huang et al. https://doi.org/10.1080/10095020.2025.2550497
- Quantifying thermal power plants CO2 emissions globally from space using hyperspectral imagers M. Lei et al. https://doi.org/10.1016/j.rsase.2025.101822
- Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. https://doi.org/10.1021/acs.est.4c07415
- Reconstructing long-term (2003–2019) global high-resolution XCO 2 : bridging observational gaps with machine learning S. Hwang et al. https://doi.org/10.1080/15481603.2026.2627042
- Satellites in addressing climate change: Trends, challenges, and future directions G. Zhou et al. https://doi.org/10.1007/s42524-026-5183-6
- Attributing GHG emissions to individual facilities using multi-temporal hyperspectral images: Methodology and applications Y. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2026.01.014
21 citations as recorded by crossref.
- Quantifying CO2 emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1 G. Han et al. https://doi.org/10.1016/j.rse.2024.114368
- 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
- High-resolution observations of NO2 and CO2 emission plumes from EnMAP satellite measurements C. Borger et al. https://doi.org/10.1088/1748-9326/adc0b1
- Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning H. Couture et al. https://doi.org/10.3390/rs16071290
- The Total Emission Estimation of Thermal Power Plants Using a Top-Down Approach Strongly Impacted by Satellite Spatial Resolution, Precision, and Monitoring Frequency X. Li et al. https://doi.org/10.34133/remotesensing.0469
- Achieving net-zero carbon in the pulp and paper industry: Tackling scope 1, 2, and 3 emission hotspots N. Barrios et al. https://doi.org/10.1016/j.rcradv.2026.200318
- Facility-Scale Detection and Quantification of Gas Flaring Using Imaging Spectrometers J. Kim et al. https://doi.org/10.1021/acsestair.6c00181
- Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches C. He et al. https://doi.org/10.1016/j.rse.2024.114192
- Global gridded NOx emissions using TROPOMI observations A. Rey-Pommier et al. https://doi.org/10.5194/essd-17-3329-2025
- 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
- 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
- Real-time monitoring of daily carbon emissions and electricity generation from fossil fuel power plants using geostationary satellite band data and deep learning techniques H. Mo et al. https://doi.org/10.1016/j.energy.2025.137570
- Facility-Scale CO2 Emission Quantification for Coal-Fired Power Plants Using Multisource Hyperspectral Satellite Observations L. Qi et al. https://doi.org/10.1109/LGRS.2026.3709724
- 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
- HyperGas 1.0: a python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantification X. Zhang et al. https://doi.org/10.5194/gmd-19-5979-2026
- Advanced satellite-based model for precise measurement of point source CO 2 emission Y. Huang et al. https://doi.org/10.1080/10095020.2025.2550497
- Quantifying thermal power plants CO2 emissions globally from space using hyperspectral imagers M. Lei et al. https://doi.org/10.1016/j.rsase.2025.101822
- Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. https://doi.org/10.1021/acs.est.4c07415
- Reconstructing long-term (2003–2019) global high-resolution XCO 2 : bridging observational gaps with machine learning S. Hwang et al. https://doi.org/10.1080/15481603.2026.2627042
- Satellites in addressing climate change: Trends, challenges, and future directions G. Zhou et al. https://doi.org/10.1007/s42524-026-5183-6
- Attributing GHG emissions to individual facilities using multi-temporal hyperspectral images: Methodology and applications Y. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2026.01.014
Saved (final revised paper)
Latest update: 29 Jul 2026
Short summary
Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we tasked two satellites to routinely observe CO2 emissions at 30 coal-fired power plants between 2021 and 2022. These results present the largest dataset of space-based CO2 emission estimates to date.
Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the...
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