Articles | Volume 16, issue 10
https://doi.org/10.5194/amt-16-2627-2023
https://doi.org/10.5194/amt-16-2627-2023
Research article
 | 
30 May 2023
Research article |  | 30 May 2023

Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images

Peter Joyce, Cristina Ruiz Villena, Yahui Huang, Alex Webb, Manuel Gloor, Fabien H. Wagner, Martyn P. Chipperfield, Rocío Barrio Guilló, Chris Wilson, and Hartmut Boesch

Related authors

Greenhouse gas retrievals for the CO2M mission using the FOCAL method: first performance estimates
Stefan Noël, Michael Buchwitz, Michael Hilker, Maximilian Reuter, Michael Weimer, Heinrich Bovensmann, John P. Burrows, Hartmut Bösch, and Ruediger Lang
Atmos. Meas. Tech., 17, 2317–2334, https://doi.org/10.5194/amt-17-2317-2024,https://doi.org/10.5194/amt-17-2317-2024, 2024
Short summary
Quantifying the tropospheric ozone radiative effect and its temporal evolution in the satellite era
Richard J. Pope, Alexandru Rap, Matilda A. Pimlott, Brice Barret, Eric Le Flochmoen, Brian J. Kerridge, Richard Siddans, Barry G. Latter, Lucy J. Ventress, Anne Boynard, Christian Retscher, Wuhu Feng, Richard Rigby, Sandip S. Dhomse, Catherine Wespes, and Martyn P. Chipperfield
Atmos. Chem. Phys., 24, 3613–3626, https://doi.org/10.5194/acp-24-3613-2024,https://doi.org/10.5194/acp-24-3613-2024, 2024
Short summary
Satellite-observed relationships between land cover, burned area and atmospheric composition over the southern Amazon
Emma Sands, Richard Pope, Ruth M. Doherty, Fiona M. O'Connor, Chris Wilson, and Hugh Pumphrey
EGUsphere, https://doi.org/10.5194/egusphere-2024-503,https://doi.org/10.5194/egusphere-2024-503, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK
Emily Dowd, Alistair J. Manning, Bryn Orth-Lashley, Marianne Girard, James France, Rebecca E. Fisher, Dave Lowry, Mathias Lanoisellé, Joseph R. Pitt, Kieran M. Stanley, Simon O'Doherty, Dickon Young, Glen Thistlethwaite, Martyn P. Chipperfield, Emanuel Gloor, and Chris Wilson
Atmos. Meas. Tech., 17, 1599–1615, https://doi.org/10.5194/amt-17-1599-2024,https://doi.org/10.5194/amt-17-1599-2024, 2024
Short summary
On the atmospheric budget of ethylene dichloride and its impact on stratospheric chlorine and ozone (2002–2020)
Ryan Hossaini, David Sherry, Zihao Wang, Martyn Chipperfield, Wuhu Feng, David Oram, Karina Adcock, Stephen Montzka, Isobel Simpson, Andrea Mazzeo, Amber Leeson, Elliot Atlas, and Charles C.-K. Chou
EGUsphere, https://doi.org/10.5194/egusphere-2024-560,https://doi.org/10.5194/egusphere-2024-560, 2024
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B
Zhonghua He, Ling Gao, Miao Liang, and Zhao-Cheng Zeng
Atmos. Meas. Tech., 17, 2937–2956, https://doi.org/10.5194/amt-17-2937-2024,https://doi.org/10.5194/amt-17-2937-2024, 2024
Short summary
Global retrieval of stratospheric and tropospheric BrO columns from the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on board the Suomi-NPP satellite
Heesung Chong, Gonzalo González Abad, Caroline R. Nowlan, Christopher Chan Miller, Alfonso Saiz-Lopez, Rafael P. Fernandez, Hyeong-Ahn Kwon, Zolal Ayazpour, Huiqun Wang, Amir H. Souri, Xiong Liu, Kelly Chance, Ewan O'Sullivan, Jhoon Kim, Ja-Ho Koo, William R. Simpson, François Hendrick, Richard Querel, Glen Jaross, Colin Seftor, and Raid M. Suleiman
Atmos. Meas. Tech., 17, 2873–2916, https://doi.org/10.5194/amt-17-2873-2024,https://doi.org/10.5194/amt-17-2873-2024, 2024
Short summary
IMK–IAA MIPAS retrieval version 8: CH4 and N2O
Norbert Glatthor, Thomas von Clarmann, Bernd Funke, Maya García-Comas, Udo Grabowski, Michael Höpfner, Sylvia Kellmann, Michael Kiefer, Alexandra Laeng, Andrea Linden, Manuel López-Puertas, and Gabriele P. Stiller
Atmos. Meas. Tech., 17, 2849–2871, https://doi.org/10.5194/amt-17-2849-2024,https://doi.org/10.5194/amt-17-2849-2024, 2024
Short summary
Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak
Matthieu Dogniaux, Joannes D. Maasakkers, Daniel J. Varon, and Ilse Aben
Atmos. Meas. Tech., 17, 2777–2787, https://doi.org/10.5194/amt-17-2777-2024,https://doi.org/10.5194/amt-17-2777-2024, 2024
Short summary
U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers
Jack H. Bruno, Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob
Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024,https://doi.org/10.5194/amt-17-2625-2024, 2024
Short summary

Cited articles

Allen, D. T., Torres, V. M., Thomas, J., Sullivan, D. W., Harrison, M., Hendler, A., Herndon, S. C., Kolb, C. E., Fraser, M. P., and Hill, A. D.: Measurements of methane emissions at natural gas production sites in the United States, P. Natl. Acad. Sci. USA, 110, 17768–17773, 2013. 
Alvarez, R. A., Zavala-Araiza, D., Lyon, D. R., Allen, D. T., Barkley, Z. R., Brandt, A. R., Davis, K. J., Herndon, S. C., Jacob, D. J., and Karion, A.: Assessment of methane emissions from the US oil and gas supply chain, Science, 361, 186–188, 2018. 
Brandt, A. R., Heath, G. A., and Cooley, D.: Methane leaks from natural gas systems follow extreme distributions, Environ. Sci. Technol., 50, 12512–12520, 2016. 
Chollet, F.: Keras: Deep Learning for humans, Release 2.12.0, GitHub [code], https://github.com/keras-team/keras (last access: 1 May 2023), 2015. 
Cusworth, D. H., Jacob, D. J., Varon, D. J., Chan Miller, C., Liu, X., Chance, K., Thorpe, A. K., Duren, R. M., Miller, C. E., Thompson, D. R., Frankenberg, C., Guanter, L., and Randles, C. A.: Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space, Atmos. Meas. Tech., 12, 5655–5668, https://doi.org/10.5194/amt-12-5655-2019, 2019. 
Download
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.