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
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Cited
13 citations as recorded by crossref.
- U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. 10.5194/amt-17-2625-2024
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. 10.1007/s11783-025-1922-x
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. 10.5194/gmd-17-4773-2024
- Sensitivity and Uncertainty in Matched-Filter-Based Gas Detection With Imaging Spectroscopy J. Fahlen et al. 10.1109/TGRS.2024.3440174
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. 10.9719/EEG.2024.57.5.513
- Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. 10.1109/LGRS.2024.3379119
- Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert 10.1038/s41467-024-47754-y
- High-Resolution Methane Mapping With the EnMAP Satellite Imaging Spectroscopy Mission J. Roger et al. 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. 10.1080/01431161.2024.2421946
- Monitoring and regression analysis of landfill surface temperatures using remote sensing and image processing techniques K. Sharma et al. 10.1080/01431161.2024.2372081
- Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images P. Joyce et al. 10.5194/amt-16-2627-2023
- Semantic segmentation of methane plumes with hyperspectral machine learning models V. Růžička et al. 10.1038/s41598-023-44918-6
10 citations as recorded by crossref.
- U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. 10.5194/amt-17-2625-2024
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. 10.1007/s11783-025-1922-x
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. 10.5194/gmd-17-4773-2024
- Sensitivity and Uncertainty in Matched-Filter-Based Gas Detection With Imaging Spectroscopy J. Fahlen et al. 10.1109/TGRS.2024.3440174
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. 10.9719/EEG.2024.57.5.513
- Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. 10.1109/LGRS.2024.3379119
- Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert 10.1038/s41467-024-47754-y
- High-Resolution Methane Mapping With the EnMAP Satellite Imaging Spectroscopy Mission J. Roger et al. 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. 10.1080/01431161.2024.2421946
3 citations as recorded by crossref.
- Monitoring and regression analysis of landfill surface temperatures using remote sensing and image processing techniques K. Sharma et al. 10.1080/01431161.2024.2372081
- Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images P. Joyce et al. 10.5194/amt-16-2627-2023
- Semantic segmentation of methane plumes with hyperspectral machine learning models V. Růžička et al. 10.1038/s41598-023-44918-6
Latest update: 13 Dec 2024
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...
