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
19 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
- Hundreds of methane super-sources pinpointed in satellite data https://doi.org/10.1038/d41586-024-03143-5
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- 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-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
- 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
- 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
- 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
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
- 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
- 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
- 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
- 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
- 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
- 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
- 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
19 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
- Hundreds of methane super-sources pinpointed in satellite data https://doi.org/10.1038/d41586-024-03143-5
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- 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-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
- 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
- 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
- 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
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
- 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
- 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
- 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
- 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
- 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
- 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
- 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: 11 Jun 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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