Articles | Volume 24, issue 18
https://doi.org/10.5194/acp-24-10441-2024
© 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/acp-24-10441-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2024
Research article |
| 19 Sep 2024
| 19 Sep 2024
Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data
Steffen Vanselow
CORRESPONDING AUTHOR
Institute of Environmental Physics (IUP), University of Bremen, FB1 Bremen, Germany
Oliver Schneising
Institute of Environmental Physics (IUP), University of Bremen, FB1 Bremen, Germany
Michael Buchwitz
Institute of Environmental Physics (IUP), University of Bremen, FB1 Bremen, Germany
Maximilian Reuter
Institute of Environmental Physics (IUP), University of Bremen, FB1 Bremen, Germany
Heinrich Bovensmann
Institute of Environmental Physics (IUP), University of Bremen, FB1 Bremen, Germany
Hartmut Boesch
Institute of Environmental Physics (IUP), University of Bremen, FB1 Bremen, Germany
John P. Burrows
Institute of Environmental Physics (IUP), University of Bremen, FB1 Bremen, Germany
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Cited
26 citations as recorded by crossref.
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- African peatlands: the untapped knowledge potential A. Gallego-Sala et al. https://doi.org/10.1098/rstb.2024.0485
- Satellite-driven assessment of methane trends, seasonal variability, and emission hotspots in Botswana’s Central and Ngamiland Regions B. Masocha & P. Mhangara https://doi.org/10.1007/s10661-025-14609-y
- A global dataset of δ13C-CH4 source signatures and associated uncertainties (1998–2022), with a sensitivity analysis to support isotopic inversions E. Tapin et al. https://doi.org/10.5194/essd-18-4793-2026
- Dynamic fusion of medium-resolution optical and SAR imagery for methane source infrastructure classification Y. He et al. https://doi.org/10.1016/j.jag.2025.104876
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- Environmental drivers constraining the seasonal variability in satellite-observed and modelled methane at northern high latitudes E. Kivimäki et al. https://doi.org/10.5194/bg-22-5193-2025
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
- A Satellite-Based Assessment of Atmospheric Methane over Ghana Using Sentinel-5P TROPOMI N. OSEI-ESSAH et al. https://doi.org/10.7886/hgs.101.1
- Unveiling cascading lag effects of wetland methane emissions: Evidence from Lake Chad in Africa R. Liu et al. https://doi.org/10.1126/sciadv.adx9866
- A Data Analytics Approach for Unraveling the Complexity of Methane Emissions: A Permian Basin Study J. Bian et al. https://doi.org/10.2118/228293-PA
- 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
- Estimating Methane Emissions by Integrating Satellite Regional Emissions Mapping and Point-Source Observations: Case Study in the Permian Basin M. Gao & Z. Xing https://doi.org/10.3390/rs17183143
- Analysis of methane loss rate in the atmosphere over Iran: A regional computational study using CAMS and ERA5 data P. Hamidi Rad & B. Hejazi https://doi.org/10.1007/s00704-026-06495-2
- Hundreds of methane super-sources pinpointed in satellite data https://doi.org/10.1038/d41586-024-03143-5
- Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al. https://doi.org/10.1021/acs.estlett.5c01140
- Satellite observations indicate a declining trend of methane emissions from heavy oil production in Canada Z. Xing et al. https://doi.org/10.1021/acs.estlett.5c00426
- 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
- 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
- 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
- Predicting and correcting the influence of boundary conditions in regional inverse analyses H. Nesser et al. https://doi.org/10.5194/gmd-18-9279-2025
- Continental-scale spatiotemporal assessment of atmospheric methane over Australia: Hotspot persistence and priority-area screening A. Ghahremanlou et al. https://doi.org/10.1016/j.atmosenv.2026.121992
- Enhancing the Detection of Potential Anthropogenic Methane Emission Sources in China Using Machine Learning and TROPOMI Observations S. Yu et al. https://doi.org/10.1021/acs.estlett.6c00115
- 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
- Surveying methane point-source super-emissions across oil and gas basins with MethaneSAT L. Guanter et al. https://doi.org/10.5194/acp-26-2941-2026
26 citations as recorded by crossref.
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- African peatlands: the untapped knowledge potential A. Gallego-Sala et al. https://doi.org/10.1098/rstb.2024.0485
- Satellite-driven assessment of methane trends, seasonal variability, and emission hotspots in Botswana’s Central and Ngamiland Regions B. Masocha & P. Mhangara https://doi.org/10.1007/s10661-025-14609-y
- A global dataset of δ13C-CH4 source signatures and associated uncertainties (1998–2022), with a sensitivity analysis to support isotopic inversions E. Tapin et al. https://doi.org/10.5194/essd-18-4793-2026
- Dynamic fusion of medium-resolution optical and SAR imagery for methane source infrastructure classification Y. He et al. https://doi.org/10.1016/j.jag.2025.104876
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- Environmental drivers constraining the seasonal variability in satellite-observed and modelled methane at northern high latitudes E. Kivimäki et al. https://doi.org/10.5194/bg-22-5193-2025
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
- A Satellite-Based Assessment of Atmospheric Methane over Ghana Using Sentinel-5P TROPOMI N. OSEI-ESSAH et al. https://doi.org/10.7886/hgs.101.1
- Unveiling cascading lag effects of wetland methane emissions: Evidence from Lake Chad in Africa R. Liu et al. https://doi.org/10.1126/sciadv.adx9866
- A Data Analytics Approach for Unraveling the Complexity of Methane Emissions: A Permian Basin Study J. Bian et al. https://doi.org/10.2118/228293-PA
- 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
- Estimating Methane Emissions by Integrating Satellite Regional Emissions Mapping and Point-Source Observations: Case Study in the Permian Basin M. Gao & Z. Xing https://doi.org/10.3390/rs17183143
- Analysis of methane loss rate in the atmosphere over Iran: A regional computational study using CAMS and ERA5 data P. Hamidi Rad & B. Hejazi https://doi.org/10.1007/s00704-026-06495-2
- Hundreds of methane super-sources pinpointed in satellite data https://doi.org/10.1038/d41586-024-03143-5
- Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al. https://doi.org/10.1021/acs.estlett.5c01140
- Satellite observations indicate a declining trend of methane emissions from heavy oil production in Canada Z. Xing et al. https://doi.org/10.1021/acs.estlett.5c00426
- 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
- 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
- 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
- Predicting and correcting the influence of boundary conditions in regional inverse analyses H. Nesser et al. https://doi.org/10.5194/gmd-18-9279-2025
- Continental-scale spatiotemporal assessment of atmospheric methane over Australia: Hotspot persistence and priority-area screening A. Ghahremanlou et al. https://doi.org/10.1016/j.atmosenv.2026.121992
- Enhancing the Detection of Potential Anthropogenic Methane Emission Sources in China Using Machine Learning and TROPOMI Observations S. Yu et al. https://doi.org/10.1021/acs.estlett.6c00115
- 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
- Surveying methane point-source super-emissions across oil and gas basins with MethaneSAT L. Guanter et al. https://doi.org/10.5194/acp-26-2941-2026
Saved (final revised paper)
Latest update: 12 Sep 2026
Short summary
We developed an algorithm to automatically detect persistent methane source regions, to quantify their emissions and to determine their source types, by analyzing TROPOMI data from 2018–2021. The over 200 globally detected natural and anthropogenic source regions include small-scale point sources such as individual coal mines and larger-scale source regions such as wetlands and large oil and gas fields.
We developed an algorithm to automatically detect persistent methane source regions, to quantify...
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