Articles | Volume 17, issue 9
https://doi.org/10.5194/amt-17-2583-2024
© 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/amt-17-2583-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2024
Research article |
| 03 May 2024
| 03 May 2024
CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery
Anna Vaughan
CORRESPONDING AUTHOR
Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, UK
Gonzalo Mateo-García
Trillium Technologies Ltd., London EC2N 2AX, UK
Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain
Luis Gómez-Chova
Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain
Vít Růžička
Department of Computer Science, University of Oxford, Oxford OX1 2JD, UK
Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain
Environmental Defense Fund, Reguliersgracht 79, 1017 LN Amsterdam, the Netherlands
Itziar Irakulis-Loitxate
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain
United Nations Environment Programme, International Methane Emissions Observatory, 1, rue Miollis, Building VII 75015 Paris, France
Viewed
Total article views: 10,097 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 May 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 7,086 | 2,816 | 195 | 10,097 | 222 | 361 |
- HTML: 7,086
- PDF: 2,816
- XML: 195
- Total: 10,097
- BibTeX: 222
- EndNote: 361
Total article views: 4,892 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 03 May 2024)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,862 | 933 | 97 | 4,892 | 146 | 164 |
- HTML: 3,862
- PDF: 933
- XML: 97
- Total: 4,892
- BibTeX: 146
- EndNote: 164
Total article views: 5,205 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 May 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,224 | 1,883 | 98 | 5,205 | 76 | 197 |
- HTML: 3,224
- PDF: 1,883
- XML: 98
- Total: 5,205
- BibTeX: 76
- EndNote: 197
Viewed (geographical distribution)
Total article views: 10,097 (including HTML, PDF, and XML)
Thereof 9,995 with geography defined
and 102 with unknown origin.
Total article views: 4,892 (including HTML, PDF, and XML)
Thereof 4,801 with geography defined
and 91 with unknown origin.
Total article views: 5,205 (including HTML, PDF, and XML)
Thereof 5,194 with geography defined
and 11 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
16 citations as recorded by crossref.
- 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
- Review of methane emission source tracing methods in oilfield regions Y. Liu et al. https://doi.org/10.1016/j.jgsce.2025.205708
- Considering the observation and illumination angular configuration for an improved detection and quantification of methane emissions J. Gorroño et al. https://doi.org/10.5194/amt-19-1245-2026
- FUMESNet: Exploring Frequency-Based Transformer and Improving Skip Connection for Hyperspectral Methane Plume Segmentation A. Dixit & P. Gupta https://doi.org/10.1109/TIM.2026.3667330
- SAM4CH4: Zero-Shot Methane Plume Mapping With Segment Anything and Vision-Language Models M. Mahdianpari et al. https://doi.org/10.1109/JSTARS.2025.3642040
- A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite S. Zhao et al. https://doi.org/10.5194/acp-25-4035-2025
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al. https://doi.org/10.1016/j.isprsjprs.2025.03.020
- Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3523022
- Towards operational automated greenhouse gas plume detection and delineation B. Bue et al. https://doi.org/10.1016/j.rse.2026.115506
- Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data X. Cai et al. https://doi.org/10.3390/atmos16050532
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- 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
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al. https://doi.org/10.5194/gmd-19-1683-2026
- Sensitivity of Airborne Methane Retrieval Algorithms (MF, ACRWL1MF, and DOAS) to Surface Albedo and Types: Hyperspectral Simulation Assessment J. Chen et al. https://doi.org/10.3390/atmos16111224
- Machine Learning in Forecasting Methane Concentration from Satellite Data K. Nowak et al. https://doi.org/10.1088/1742-6596/3107/1/012020
16 citations as recorded by crossref.
- 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
- Review of methane emission source tracing methods in oilfield regions Y. Liu et al. https://doi.org/10.1016/j.jgsce.2025.205708
- Considering the observation and illumination angular configuration for an improved detection and quantification of methane emissions J. Gorroño et al. https://doi.org/10.5194/amt-19-1245-2026
- FUMESNet: Exploring Frequency-Based Transformer and Improving Skip Connection for Hyperspectral Methane Plume Segmentation A. Dixit & P. Gupta https://doi.org/10.1109/TIM.2026.3667330
- SAM4CH4: Zero-Shot Methane Plume Mapping With Segment Anything and Vision-Language Models M. Mahdianpari et al. https://doi.org/10.1109/JSTARS.2025.3642040
- A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite S. Zhao et al. https://doi.org/10.5194/acp-25-4035-2025
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al. https://doi.org/10.1016/j.isprsjprs.2025.03.020
- Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3523022
- Towards operational automated greenhouse gas plume detection and delineation B. Bue et al. https://doi.org/10.1016/j.rse.2026.115506
- Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data X. Cai et al. https://doi.org/10.3390/atmos16050532
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- 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
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al. https://doi.org/10.5194/gmd-19-1683-2026
- Sensitivity of Airborne Methane Retrieval Algorithms (MF, ACRWL1MF, and DOAS) to Surface Albedo and Types: Hyperspectral Simulation Assessment J. Chen et al. https://doi.org/10.3390/atmos16111224
- Machine Learning in Forecasting Methane Concentration from Satellite Data K. Nowak et al. https://doi.org/10.1088/1742-6596/3107/1/012020
Saved (final revised paper)
Latest update: 21 Jun 2026
Short summary
Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.
Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming...
