Articles | Volume 16, issue 16
https://doi.org/10.5194/amt-16-3787-2023
© 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/amt-16-3787-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Aug 2023
Research article |
| 18 Aug 2023
| 18 Aug 2023
A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases
Nicholas Balasus
CORRESPONDING AUTHOR
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Daniel J. Jacob
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Department of Earth and Planetary Sciences, Harvard University, Cambridge, USA
Alba Lorente
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Joannes D. Maasakkers
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Robert J. Parker
National Centre for Earth Observation, University of Leicester, Leicester, UK
Earth Observation Science, School of Physics and Astronomy, University of Leicester, Leicester, UK
Hartmut Boesch
National Centre for Earth Observation, University of Leicester, Leicester, UK
Earth Observation Science, School of Physics and Astronomy, University of Leicester, Leicester, UK
now at: Institute of Environmental Physics (IUP), University of Bremen FB1, Bremen, Germany
Zichong Chen
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Makoto M. Kelp
Department of Earth and Planetary Sciences, Harvard University, Cambridge, USA
Hannah Nesser
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Daniel J. Varon
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Viewed
Total article views: 9,783 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Mar 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 7,441 | 2,181 | 161 | 9,783 | 216 | 225 |
- HTML: 7,441
- PDF: 2,181
- XML: 161
- Total: 9,783
- BibTeX: 216
- EndNote: 225
Total article views: 7,813 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Aug 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 6,326 | 1,358 | 129 | 7,813 | 191 | 199 |
- HTML: 6,326
- PDF: 1,358
- XML: 129
- Total: 7,813
- BibTeX: 191
- EndNote: 199
Total article views: 1,970 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Mar 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,115 | 823 | 32 | 1,970 | 25 | 26 |
- HTML: 1,115
- PDF: 823
- XML: 32
- Total: 1,970
- BibTeX: 25
- EndNote: 26
Viewed (geographical distribution)
Total article views: 9,783 (including HTML, PDF, and XML)
Thereof 9,503 with geography defined
and 280 with unknown origin.
Total article views: 7,813 (including HTML, PDF, and XML)
Thereof 7,530 with geography defined
and 283 with unknown origin.
Total article views: 1,970 (including HTML, PDF, and XML)
Thereof 1,970 with geography defined
and 0 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
57 citations as recorded by crossref.
- Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions S. Zhu et al. https://doi.org/10.3390/rs17132321
- 基于地面与机载测量的青藏高原CO<sub>2</sub>与CH<sub>4</sub>浓度助力卫星数据验证初探 宜. 汪 & 向. 田 https://doi.org/10.1360/N072025-0040
- Emerging Trends, Sectoral, and Regional Patterns in China’s Methane Emissions: A Satellite-Constrained Perspective D. Chen et al. https://doi.org/10.1021/acsestair.5c00405
- Global methane emissions rebounded in 2024 despite a deceleration in atmospheric growth Y. Wang et al. https://doi.org/10.1038/s41467-026-72764-3
- Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
- Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates J. East et al. https://doi.org/10.1038/s41467-025-67122-8
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Gap-filled spatiotemporal reconstruction of XCH4 data and analysis of methane emission patterns Q. Xiao et al. https://doi.org/10.1016/j.apr.2026.102918
- Global daily TROPOMI XCH₄ reconstruction and methane emission hotspot identification using machine learning Q. Xiao et al. https://doi.org/10.1080/17538947.2026.2677964
- Strong monsoon influence on South Asian methane emissions in 2020 revealed by a Bayesian inversion constrained by satellite observations R. Subramanian et al. https://doi.org/10.5194/acp-26-9757-2026
- Bridging the gap: Ground-based and airborne measurements of CO2 and CH4 over the Tibetan Plateau for satellite validation Y. Wang & X. Tian https://doi.org/10.1007/s11430-025-1553-9
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- The added value of new ground-based observations in improving China's methane emission quantification H. Zhong et al. https://doi.org/10.5194/amt-19-4759-2026
- Duration of super-emitting oil and gas methane sources D. Cusworth et al. https://doi.org/10.1038/s41467-026-68804-7
- Quantifying national, state, and oil/gas field methane emissions and trends in the US (2019–2024) through high resolution inversion of satellite observations L. Estrada et al. https://doi.org/10.5194/acp-26-10629-2026
- Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe R. Padilla et al. https://doi.org/10.1016/j.atmosenv.2024.120665
- Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al. https://doi.org/10.3390/atmos16030279
- Quantifying methane emission baselines with high-resolution satellite data to support China’s emission control H. Zhong et al. https://doi.org/10.1016/j.scib.2025.04.047
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions B. Zheng et al. https://doi.org/10.5194/acp-26-1931-2026
- Assessment of the differences in European CH4 emission estimates from three TROPOMI products A. Sicsik-Paré et al. https://doi.org/10.5194/acp-26-10423-2026
- Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. https://doi.org/10.3390/rs17030543
- State of the Art in Monitoring Methane Emissions from Arctic–boreal Wetlands and Lakes M. Mahdianpari et al. https://doi.org/10.3390/rs18060926
- 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
- Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT T. Cai & C. Xiang https://doi.org/10.3390/atmos15030266
- Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations D. Pendergrass et al. https://doi.org/10.5194/acp-25-14353-2025
- Reducing uncertainties of global methane flux products under extreme climatic conditions using explainable automated machine learning X. Yu et al. https://doi.org/10.1016/j.nexus.2026.100753
- Global Daily Atmospheric Methane Column Concentration Retrieval From TROPOMI Satellite Spectral Measurements by Two-Stage Machine Learning Algorithm Y. Bao et al. https://doi.org/10.1109/JSTARS.2026.3688284
- MCF-XCO2: A cross-mission consistency and fusion framework for integrating multi-satellite XCO2 observations Y. Yu et al. https://doi.org/10.1016/j.atmosres.2026.108747
- Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning H. Bi & S. Neethirajan https://doi.org/10.3390/cli12120223
- Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. https://doi.org/10.5194/acp-25-797-2025
- Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. https://doi.org/10.1016/j.jenvman.2025.126309
- Multi-modal neural fusion for accurate carbon dioxide column sensing using laser heterodyne radiometry H. Xiong et al. https://doi.org/10.1016/j.snb.2026.139973
- Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction A. Bradley et al. https://doi.org/10.5194/amt-18-1675-2025
- Satellite monitoring of annual US landfill methane emissions and trends N. Balasus et al. https://doi.org/10.1088/1748-9326/ada2b1
- Satellite-Derived Estimates on Methane Emissions From Rice Paddies Across China X. Zhang et al. https://doi.org/10.1109/TGRS.2026.3665059
- Correction of near-surface methane concentrations using a CNN-RF hybrid model based on multi-scale feature extraction L. Fan et al. https://doi.org/10.1016/j.apr.2026.103078
- Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India T. Mathew et al. https://doi.org/10.5194/acp-26-4453-2026
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. https://doi.org/10.5194/amt-17-5147-2024
- Spatial distribution pattern and long-term trend of atmospheric methane in the Atlantic-Mediterranean transition region based on TROPOMI and GOSAT measurements J. Adame et al. https://doi.org/10.1016/j.scitotenv.2024.178006
- Seasonality and Declining Intensity of Methane Emissions from the Permian and Nearby US Oil and Gas Basins D. Varon et al. https://doi.org/10.1021/acs.est.5c08745
- TROPOMI/WFMD v2.0: Improved retrievals of XCH4 and XCO with XGBoost-based quality filtering O. Schneising et al. https://doi.org/10.5194/amt-19-2407-2026
- Multiscale attribution of atmospheric methane variability in China using satellite observations and interpretable machine learning D. Singh et al. https://doi.org/10.1016/j.jenvman.2026.130325
- Enhanced methane monitoring: a globally harmonized daily 0.1° XCH4 through machine learning-based fusion of GOSAT, GOSAT-2, and TROPOMI J. Keya et al. https://doi.org/10.5194/amt-19-4313-2026
- High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills H. Nesser et al. https://doi.org/10.5194/acp-24-5069-2024
- Applying satellite observations to improve bottom-up national emission inventories for methane: application to Colombia S. Hancock et al. https://doi.org/10.5194/acp-26-10455-2026
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
- Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. https://doi.org/10.1016/j.rse.2024.114039
- Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations L. Estrada et al. https://doi.org/10.5194/gmd-18-3311-2025
- Review of methane emission source tracing methods in oilfield regions Y. Liu et al. https://doi.org/10.1016/j.jgsce.2025.205708
- Innovative software for analysing satellite data and methane emissions using radiative transfer model K. Aghayeva & G. Krauklit https://doi.org/10.62660/bcstu/3.2024.65
- Quantifying urban and landfill methane emissions in the United States using TROPOMI satellite data X. Wang et al. https://doi.org/10.1126/sciadv.adz9308
- Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan https://doi.org/10.3390/su162310400
- Attributing 2019–2024 methane growth using TROPOMI satellite observations M. He et al. https://doi.org/10.1126/sciadv.adz9007
- 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
- Rapid summer methane emission decline in high-latitude plains linked to 2021 drought M. Zhao et al. https://doi.org/10.1038/s43247-026-03433-y
- 2019–2024 trends in African livestock and wetland emissions as contributors to the global methane rise N. Balasus et al. https://doi.org/10.5194/acp-26-4601-2026
57 citations as recorded by crossref.
- Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions S. Zhu et al. https://doi.org/10.3390/rs17132321
- 基于地面与机载测量的青藏高原CO<sub>2</sub>与CH<sub>4</sub>浓度助力卫星数据验证初探 宜. 汪 & 向. 田 https://doi.org/10.1360/N072025-0040
- Emerging Trends, Sectoral, and Regional Patterns in China’s Methane Emissions: A Satellite-Constrained Perspective D. Chen et al. https://doi.org/10.1021/acsestair.5c00405
- Global methane emissions rebounded in 2024 despite a deceleration in atmospheric growth Y. Wang et al. https://doi.org/10.1038/s41467-026-72764-3
- Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
- Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates J. East et al. https://doi.org/10.1038/s41467-025-67122-8
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Gap-filled spatiotemporal reconstruction of XCH4 data and analysis of methane emission patterns Q. Xiao et al. https://doi.org/10.1016/j.apr.2026.102918
- Global daily TROPOMI XCH₄ reconstruction and methane emission hotspot identification using machine learning Q. Xiao et al. https://doi.org/10.1080/17538947.2026.2677964
- Strong monsoon influence on South Asian methane emissions in 2020 revealed by a Bayesian inversion constrained by satellite observations R. Subramanian et al. https://doi.org/10.5194/acp-26-9757-2026
- Bridging the gap: Ground-based and airborne measurements of CO2 and CH4 over the Tibetan Plateau for satellite validation Y. Wang & X. Tian https://doi.org/10.1007/s11430-025-1553-9
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- The added value of new ground-based observations in improving China's methane emission quantification H. Zhong et al. https://doi.org/10.5194/amt-19-4759-2026
- Duration of super-emitting oil and gas methane sources D. Cusworth et al. https://doi.org/10.1038/s41467-026-68804-7
- Quantifying national, state, and oil/gas field methane emissions and trends in the US (2019–2024) through high resolution inversion of satellite observations L. Estrada et al. https://doi.org/10.5194/acp-26-10629-2026
- Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe R. Padilla et al. https://doi.org/10.1016/j.atmosenv.2024.120665
- Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al. https://doi.org/10.3390/atmos16030279
- Quantifying methane emission baselines with high-resolution satellite data to support China’s emission control H. Zhong et al. https://doi.org/10.1016/j.scib.2025.04.047
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions B. Zheng et al. https://doi.org/10.5194/acp-26-1931-2026
- Assessment of the differences in European CH4 emission estimates from three TROPOMI products A. Sicsik-Paré et al. https://doi.org/10.5194/acp-26-10423-2026
- Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. https://doi.org/10.3390/rs17030543
- State of the Art in Monitoring Methane Emissions from Arctic–boreal Wetlands and Lakes M. Mahdianpari et al. https://doi.org/10.3390/rs18060926
- 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
- Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT T. Cai & C. Xiang https://doi.org/10.3390/atmos15030266
- Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations D. Pendergrass et al. https://doi.org/10.5194/acp-25-14353-2025
- Reducing uncertainties of global methane flux products under extreme climatic conditions using explainable automated machine learning X. Yu et al. https://doi.org/10.1016/j.nexus.2026.100753
- Global Daily Atmospheric Methane Column Concentration Retrieval From TROPOMI Satellite Spectral Measurements by Two-Stage Machine Learning Algorithm Y. Bao et al. https://doi.org/10.1109/JSTARS.2026.3688284
- MCF-XCO2: A cross-mission consistency and fusion framework for integrating multi-satellite XCO2 observations Y. Yu et al. https://doi.org/10.1016/j.atmosres.2026.108747
- Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning H. Bi & S. Neethirajan https://doi.org/10.3390/cli12120223
- Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. https://doi.org/10.5194/acp-25-797-2025
- Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. https://doi.org/10.1016/j.jenvman.2025.126309
- Multi-modal neural fusion for accurate carbon dioxide column sensing using laser heterodyne radiometry H. Xiong et al. https://doi.org/10.1016/j.snb.2026.139973
- Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction A. Bradley et al. https://doi.org/10.5194/amt-18-1675-2025
- Satellite monitoring of annual US landfill methane emissions and trends N. Balasus et al. https://doi.org/10.1088/1748-9326/ada2b1
- Satellite-Derived Estimates on Methane Emissions From Rice Paddies Across China X. Zhang et al. https://doi.org/10.1109/TGRS.2026.3665059
- Correction of near-surface methane concentrations using a CNN-RF hybrid model based on multi-scale feature extraction L. Fan et al. https://doi.org/10.1016/j.apr.2026.103078
- Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India T. Mathew et al. https://doi.org/10.5194/acp-26-4453-2026
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. https://doi.org/10.5194/amt-17-5147-2024
- Spatial distribution pattern and long-term trend of atmospheric methane in the Atlantic-Mediterranean transition region based on TROPOMI and GOSAT measurements J. Adame et al. https://doi.org/10.1016/j.scitotenv.2024.178006
- Seasonality and Declining Intensity of Methane Emissions from the Permian and Nearby US Oil and Gas Basins D. Varon et al. https://doi.org/10.1021/acs.est.5c08745
- TROPOMI/WFMD v2.0: Improved retrievals of XCH4 and XCO with XGBoost-based quality filtering O. Schneising et al. https://doi.org/10.5194/amt-19-2407-2026
- Multiscale attribution of atmospheric methane variability in China using satellite observations and interpretable machine learning D. Singh et al. https://doi.org/10.1016/j.jenvman.2026.130325
- Enhanced methane monitoring: a globally harmonized daily 0.1° XCH4 through machine learning-based fusion of GOSAT, GOSAT-2, and TROPOMI J. Keya et al. https://doi.org/10.5194/amt-19-4313-2026
- High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills H. Nesser et al. https://doi.org/10.5194/acp-24-5069-2024
- Applying satellite observations to improve bottom-up national emission inventories for methane: application to Colombia S. Hancock et al. https://doi.org/10.5194/acp-26-10455-2026
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
- Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. https://doi.org/10.1016/j.rse.2024.114039
- Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations L. Estrada et al. https://doi.org/10.5194/gmd-18-3311-2025
- Review of methane emission source tracing methods in oilfield regions Y. Liu et al. https://doi.org/10.1016/j.jgsce.2025.205708
- Innovative software for analysing satellite data and methane emissions using radiative transfer model K. Aghayeva & G. Krauklit https://doi.org/10.62660/bcstu/3.2024.65
- Quantifying urban and landfill methane emissions in the United States using TROPOMI satellite data X. Wang et al. https://doi.org/10.1126/sciadv.adz9308
- Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan https://doi.org/10.3390/su162310400
- Attributing 2019–2024 methane growth using TROPOMI satellite observations M. He et al. https://doi.org/10.1126/sciadv.adz9007
- 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
- Rapid summer methane emission decline in high-latitude plains linked to 2021 drought M. Zhao et al. https://doi.org/10.1038/s43247-026-03433-y
- 2019–2024 trends in African livestock and wetland emissions as contributors to the global methane rise N. Balasus et al. https://doi.org/10.5194/acp-26-4601-2026
Saved (final revised paper)
Latest update: 26 Aug 2026
Short summary
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric...
