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
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Cited
20 citations as recorded by crossref.
- Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe R. Padilla et al. 10.1016/j.atmosenv.2024.120665
- Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al. 10.3390/atmos16030279
- Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning H. Bi & S. Neethirajan 10.3390/cli12120223
- 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 10.1360/N072025-0040
- Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. 10.5194/acp-25-797-2025
- 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. 10.5194/acp-24-5069-2024
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 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. 10.1016/j.rse.2024.114039
- Innovative software for analysing satellite data and methane emissions using radiative transfer model K. Aghayeva & G. Krauklit 10.62660/bcstu/3.2024.65
- Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction A. Bradley et al. 10.5194/amt-18-1675-2025
- Satellite monitoring of annual US landfill methane emissions and trends N. Balasus et al. 10.1088/1748-9326/ada2b1
- Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan 10.3390/su162310400
- 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. 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. 10.1016/j.scitotenv.2024.178006
- Bridging the gap: Ground-based and airborne measurements of CO2 and CH4 over the Tibetan Plateau for satellite validation Y. Wang & X. Tian 10.1007/s11430-025-1553-9
- Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. 10.3390/rs17030543
- Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. 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 10.3390/atmos15030266
- Interpreting the Seasonality of Atmospheric Methane J. East et al. 10.1029/2024GL108494
- Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al. 10.1038/s41598-024-58995-8
18 citations as recorded by crossref.
- Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe R. Padilla et al. 10.1016/j.atmosenv.2024.120665
- Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al. 10.3390/atmos16030279
- Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning H. Bi & S. Neethirajan 10.3390/cli12120223
- 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 10.1360/N072025-0040
- Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. 10.5194/acp-25-797-2025
- 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. 10.5194/acp-24-5069-2024
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 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. 10.1016/j.rse.2024.114039
- Innovative software for analysing satellite data and methane emissions using radiative transfer model K. Aghayeva & G. Krauklit 10.62660/bcstu/3.2024.65
- Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction A. Bradley et al. 10.5194/amt-18-1675-2025
- Satellite monitoring of annual US landfill methane emissions and trends N. Balasus et al. 10.1088/1748-9326/ada2b1
- Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan 10.3390/su162310400
- 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. 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. 10.1016/j.scitotenv.2024.178006
- Bridging the gap: Ground-based and airborne measurements of CO2 and CH4 over the Tibetan Plateau for satellite validation Y. Wang & X. Tian 10.1007/s11430-025-1553-9
- Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. 10.3390/rs17030543
- Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. 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 10.3390/atmos15030266
Latest update: 23 Apr 2025
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...
