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

The Added Value of Amazon observations to estimate methane budget over South American river basins in a global inversion system
Shrutika Wagh, Luana S. Basso, Ayan F. Fleischmann, João Henrique Fernandes Amaral, John Melack, Jost Larvic, David Walter, Stijn Hantson, Julia Marshall, Chris Wilson, Youmi Oh, Lori Bruhwiler, John B. Miller, Corinne Vigouroux, Christian Rödenbeck, and Santiago Botía
EGUsphere, https://doi.org/10.5194/egusphere-2026-5245,https://doi.org/10.5194/egusphere-2026-5245, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Feasibility study of atmospheric carbon dioxide satellite retrievals over snow in the context of the Copernicus Anthropogenic CO2 Monitoring Mission
Amanita Mikkonen, Hartmut Bösch, Jouni Peltoniemi, Leif Vogel, Antonio Di Noia, Ella Kivimäki, Miia Salminen, Maria Gritsevich, Yasjka Meijer, and Hannakaisa Lindqvist
EGUsphere, https://doi.org/10.5194/egusphere-2026-2722,https://doi.org/10.5194/egusphere-2026-2722, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Hydrological drivers of hydrogen cyanide wildfire emissions from Indonesian peat fires during the 2015, 2019, and 2023 El Niño events
Antonio G. Bruno, David P. Moore, Jeremy J. Harrison, Ailish Graham, Martyn P. Chipperfield, and Corinne Vigouroux
Atmos. Chem. Phys., 26, 12925–12952, https://doi.org/10.5194/acp-26-12925-2026,https://doi.org/10.5194/acp-26-12925-2026, 2026
Short summary
Understanding the sources and variability of methane emissions in Madrid with Sentinel-5P TROPOMI, GHGSat, and aircraft observations
Daniel A. Potts, Harjinder Sembhi, Matthieu Dogniaux, Joannes D. Maasakkers, Berend J. Schuit, Dirk Schuettemeyer, Timon Hummel, Rocio Barrio Guillo, Oliver Schneising, Luis Guanter, and Ilse Aben
EGUsphere, https://doi.org/10.5194/egusphere-2026-3797,https://doi.org/10.5194/egusphere-2026-3797, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
A consistent climate data record of atmospheric methane from the GOSAT and GOSAT-2 missions
Robert J. Parker, Michael P. Cartwright, Dan Orr, Lakshmi N. Bharathan, Neil Humpage, Alex J. Webb, Peter Somkuti, Hartmut Boesch, Antonio Di Noia, Hiroshi Suto, Matthias Buschmann, Nicholas M. Deutscher, David W. T. Griffith, Frank Hase, Rigel Kivi, Erin McGee, Isamu Morino, Hirofumi Ohyama, Christof Petri, John Robinson, Coleen M. Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Wei Wang, Thorsten Warneke, Damien Weidmann, Debra Wunch, and Minqiang Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-625,https://doi.org/10.5194/essd-2026-625, 2026
Preprint under review for ESSD
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.
Share