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

Viewed

Total article views: 3,143 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,130 955 58 3,143 187 56 63
  • HTML: 2,130
  • PDF: 955
  • XML: 58
  • Total: 3,143
  • Supplement: 187
  • BibTeX: 56
  • EndNote: 63
Views and downloads (calculated since 02 Nov 2022)
Cumulative views and downloads (calculated since 02 Nov 2022)

Viewed (geographical distribution)

Total article views: 3,143 (including HTML, PDF, and XML) Thereof 3,306 with geography defined and -163 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Jun 2024
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.