Articles | Volume 16, issue 10
https://doi.org/10.5194/amt-16-2627-2023
© 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-16-2627-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 May 2023
Research article |
| 30 May 2023
| 30 May 2023
Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images
Peter Joyce
School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom
National Centre for Earth Observation, University of Leeds, Leeds, LS2 9JT, United Kingdom
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
Cristina Ruiz Villena
School of Physics and Astronomy, University of Leicester, Leicester, LE1 7RH, United Kingdom
National Centre for Earth Observation, University of Leicester, Leicester, LE4 5SP, United Kingdom
Yahui Huang
National Centre for Earth Observation, University of Leeds, Leeds, LS2 9JT, United Kingdom
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
Alex Webb
School of Physics and Astronomy, University of Leicester, Leicester, LE1 7RH, United Kingdom
National Centre for Earth Observation, University of Leicester, Leicester, LE4 5SP, United Kingdom
Manuel Gloor
School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom
Fabien H. Wagner
Institute of Environment and Sustainability, University of California, Los Angeles, CA 90095, USA
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove, Pasadena, CA 91109, USA
Martyn P. Chipperfield
National Centre for Earth Observation, University of Leeds, Leeds, LS2 9JT, United Kingdom
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
Rocío Barrio Guilló
School of Physics and Astronomy, University of Leicester, Leicester, LE1 7RH, United Kingdom
Chris Wilson
National Centre for Earth Observation, University of Leeds, Leeds, LS2 9JT, United Kingdom
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
School of Physics and Astronomy, University of Leicester, Leicester, LE1 7RH, United Kingdom
National Centre for Earth Observation, University of Leicester, Leicester, LE4 5SP, United Kingdom
now at: Institute of Environmental Physics, University of Bremen, 28334 Bremen, Germany
Viewed
Total article views: 7,894 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Nov 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 5,625 | 2,053 | 216 | 7,894 | 549 | 222 | 300 |
- HTML: 5,625
- PDF: 2,053
- XML: 216
- Total: 7,894
- Supplement: 549
- BibTeX: 222
- EndNote: 300
Total article views: 5,314 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 May 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,116 | 1,072 | 126 | 5,314 | 294 | 138 | 175 |
- HTML: 4,116
- PDF: 1,072
- XML: 126
- Total: 5,314
- Supplement: 294
- BibTeX: 138
- EndNote: 175
Total article views: 2,580 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Nov 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,509 | 981 | 90 | 2,580 | 255 | 84 | 125 |
- HTML: 1,509
- PDF: 981
- XML: 90
- Total: 2,580
- Supplement: 255
- BibTeX: 84
- EndNote: 125
Viewed (geographical distribution)
Total article views: 7,894 (including HTML, PDF, and XML)
Thereof 7,894 with geography defined
and 0 with unknown origin.
Total article views: 5,314 (including HTML, PDF, and XML)
Thereof 5,295 with geography defined
and 19 with unknown origin.
Total article views: 2,580 (including HTML, PDF, and XML)
Thereof 2,580 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
39 citations as recorded by crossref.
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
- 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
- Quantifying CH4 point source emissions with airborne remote sensing: first results from AVIRIS-4 S. Meier et al. https://doi.org/10.5194/amt-19-333-2026
- Two-stage offline knowledge distillation for onboard registration of multispectral satellite images D. Priyasad et al. https://doi.org/10.1016/j.isprsjprs.2025.12.009
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-17-1995-2024
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. https://doi.org/10.9719/EEG.2024.57.5.513
- N-BPMSNet: An NDMI-Guided Bitemporal Network for Methane Plume Detection and Segmentation From Sentinel-2 Multispectral Observations D. Xu et al. https://doi.org/10.1109/TGRS.2026.3689118
- Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert https://doi.org/10.1038/s41467-024-47754-y
- 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
- High-Resolution Methane Mapping With the EnMAP Satellite Imaging Spectroscopy Mission J. Roger et al. https://doi.org/10.1109/TGRS.2024.3352403
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. https://doi.org/10.1080/01431161.2024.2421946
- 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
- Dual stage – Spectral and Spatial Analyser (DSSA) - A novel deep learning model for methane detection from hyperspectral images M. George & R. Sethunadh https://doi.org/10.1016/j.rsase.2026.101926
- 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
- Multiplatform Methane Plume Detection via Model and Domain Adaptation V. Mancoridis et al. https://doi.org/10.1109/TGRS.2025.3608601
- HyperspectralViTs: General Hyperspectral Models for On-Board Remote Sensing V. Růžička & A. Markham https://doi.org/10.1109/JSTARS.2025.3557527
- 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
- Sensitivity and Uncertainty in Matched-Filter-Based Gas Detection With Imaging Spectroscopy J. Fahlen et al. https://doi.org/10.1109/TGRS.2024.3440174
- Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3523022
- MPSUNet: A Deep Learning-Based Segmentation Framework for Methane Plume Detection With Space-Based Hyperspectral and Multispectral Imagery C. Chen et al. https://doi.org/10.1109/TGRS.2025.3563599
- Satellite-Derived Approaches for Coal Mine Methane Estimation: A Review A. Chauhan & S. Raval https://doi.org/10.3390/rs17213652
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Separating and quantifying facility-level methane emissions with overlapping plumes for spaceborne methane monitoring Y. Pang et al. https://doi.org/10.5194/amt-18-455-2025
- 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
- U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. https://doi.org/10.5194/amt-17-2625-2024
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. https://doi.org/10.5194/gmd-17-4773-2024
- Evaluation of methane emission from MSW landfills in China, India, and the U.S. from space using a two-tier approach S. Zhang et al. https://doi.org/10.1016/j.jenvman.2025.124705
- CELNet: A comprehensive efficient learning network for atmospheric plume identification from remotely sensed methane concentration images F. Chen et al. https://doi.org/10.1016/j.rse.2025.114828
- 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
- 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
- Improvements of AI-driven emission estimation for point sources applied to high resolution 2-D methane-plume imagery T. Plewa et al. https://doi.org/10.1016/j.rse.2025.115002
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. https://doi.org/10.1109/LGRS.2024.3379119
- 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
- Advanced AI-driven methane emission detection, quantification, and localization in Canada: A hybrid multi-source fusion framework A. Yazdinejad et al. https://doi.org/10.1016/j.scitotenv.2025.180142
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
39 citations as recorded by crossref.
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
- 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
- Quantifying CH4 point source emissions with airborne remote sensing: first results from AVIRIS-4 S. Meier et al. https://doi.org/10.5194/amt-19-333-2026
- Two-stage offline knowledge distillation for onboard registration of multispectral satellite images D. Priyasad et al. https://doi.org/10.1016/j.isprsjprs.2025.12.009
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-17-1995-2024
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. https://doi.org/10.9719/EEG.2024.57.5.513
- N-BPMSNet: An NDMI-Guided Bitemporal Network for Methane Plume Detection and Segmentation From Sentinel-2 Multispectral Observations D. Xu et al. https://doi.org/10.1109/TGRS.2026.3689118
- Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert https://doi.org/10.1038/s41467-024-47754-y
- 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
- High-Resolution Methane Mapping With the EnMAP Satellite Imaging Spectroscopy Mission J. Roger et al. https://doi.org/10.1109/TGRS.2024.3352403
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. https://doi.org/10.1080/01431161.2024.2421946
- 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
- Dual stage – Spectral and Spatial Analyser (DSSA) - A novel deep learning model for methane detection from hyperspectral images M. George & R. Sethunadh https://doi.org/10.1016/j.rsase.2026.101926
- 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
- Multiplatform Methane Plume Detection via Model and Domain Adaptation V. Mancoridis et al. https://doi.org/10.1109/TGRS.2025.3608601
- HyperspectralViTs: General Hyperspectral Models for On-Board Remote Sensing V. Růžička & A. Markham https://doi.org/10.1109/JSTARS.2025.3557527
- 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
- Sensitivity and Uncertainty in Matched-Filter-Based Gas Detection With Imaging Spectroscopy J. Fahlen et al. https://doi.org/10.1109/TGRS.2024.3440174
- Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3523022
- MPSUNet: A Deep Learning-Based Segmentation Framework for Methane Plume Detection With Space-Based Hyperspectral and Multispectral Imagery C. Chen et al. https://doi.org/10.1109/TGRS.2025.3563599
- Satellite-Derived Approaches for Coal Mine Methane Estimation: A Review A. Chauhan & S. Raval https://doi.org/10.3390/rs17213652
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Separating and quantifying facility-level methane emissions with overlapping plumes for spaceborne methane monitoring Y. Pang et al. https://doi.org/10.5194/amt-18-455-2025
- 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
- U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. https://doi.org/10.5194/amt-17-2625-2024
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. https://doi.org/10.5194/gmd-17-4773-2024
- Evaluation of methane emission from MSW landfills in China, India, and the U.S. from space using a two-tier approach S. Zhang et al. https://doi.org/10.1016/j.jenvman.2025.124705
- CELNet: A comprehensive efficient learning network for atmospheric plume identification from remotely sensed methane concentration images F. Chen et al. https://doi.org/10.1016/j.rse.2025.114828
- 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
- 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
- Improvements of AI-driven emission estimation for point sources applied to high resolution 2-D methane-plume imagery T. Plewa et al. https://doi.org/10.1016/j.rse.2025.115002
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. https://doi.org/10.1109/LGRS.2024.3379119
- 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
- Advanced AI-driven methane emission detection, quantification, and localization in Canada: A hybrid multi-source fusion framework A. Yazdinejad et al. https://doi.org/10.1016/j.scitotenv.2025.180142
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
Methane emissions are responsible for a lot of the warming caused by the greenhouse effect, much of which comes from a small number of point sources. We can identify methane point sources by analysing satellite data, but it requires a lot of time invested by experts and is prone to very high errors. Here, we produce a neural network that can automatically identify methane point sources and estimate the mass of methane that is being released per hour and are able to do so with far smaller errors.
Methane emissions are responsible for a lot of the warming caused by the greenhouse effect, much...
