Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 18
Articles | Volume 18, issue 18
https://doi.org/10.5194/amt-18-4611-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-4611-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 18
Research article
 | 
22 Sep 2025
Research article |  | 22 Sep 2025

Tightening up methane plume source rate estimation in EnMAP and PRISMA images

Elyes Ouerghi, Thibaud Ehret, Gabriele Facciolo, Enric Meinhardt, Rodolphe Marion, and Jean-Michel Morel

Viewed

Total article views: 2,566 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,141 366 59 2,566 50 73
  • HTML: 2,141
  • PDF: 366
  • XML: 59
  • Total: 2,566
  • BibTeX: 50
  • EndNote: 73
Views and downloads (calculated since 02 Apr 2025)
Cumulative views and downloads (calculated since 02 Apr 2025)

Viewed (geographical distribution)

Total article views: 2,566 (including HTML, PDF, and XML) Thereof 2,559 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Mar 2026
Download
Short summary
Reducing methane emissions is essential to tackle climate change. In this paper, we introduce MetFluxNet, a deep learning model for methane plume source rate estimation. This model is trained on a new synthetic dataset designed to avoid network overfit. MetFluxNet can accurately estimate low source rates even in the case of heterogeneous backgrounds. To demonstrate its reliability for real-world plume estimation, we validated its predictions on real plumes with known source rates.
Read more
Share