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

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AUTOMATIC METHANE PLUMES DETECTION IN TIME SERIES OF SENTINEL-5P L1B IMAGES
E. Ouerghi, T. Ehret, C. de Franchis, G. Facciolo, T. Lauvaux, E. Meinhardt, and J.-M. Morel
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 147–154, https://doi.org/10.5194/isprs-annals-V-3-2022-147-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-147-2022, 2022
DETECTION OF METHANE PLUMES IN HYPERSPECTRAL IMAGES FROM SENTINEL-5P BY COUPLING ANOMALY DETECTION AND PATTERN RECOGNITION
E. Ouerghi, T. Ehret, C. de Franchis, G. Facciolo, T. Lauvaux, E. Meinhardt, and J.-M. Morel
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 81–87, https://doi.org/10.5194/isprs-annals-V-3-2021-81-2021,https://doi.org/10.5194/isprs-annals-V-3-2021-81-2021, 2021

Cited articles

Bruno, J. H., Jervis, D., Varon, D. J., and Jacob, D. J.: U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers, Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024, 2024. a
Cogliati, S., Sarti, F., Chiarantini, L., Cosi, M., Lorusso, R., Lopinto, E., Miglietta, F., Genesio, L., Guanter, L., Damm, A., Pérez-López, S., Scheffler, D., Tagliabue, G., Panigada, C., Rascher, U., Dowling, T., Giardino, C., and Colombo, R.: The PRISMA imaging spectroscopy mission: overview and first performance analysis, Remote Sens. Environ., 262, 112499, https://doi.org/10.1016/j.rse.2021.112499, 2021. a, b
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. a, b, c
Ehret, T., Truchis, A. D., Mazzolini, M., Morel, J.-M., d'Aspremont, A., Lauvaux, T., Duren, R., Cusworth, D., and Facciolo, G.: Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery, CoRR, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.11832, 22 October 2021. a
Foote, M. D., Dennison, P. E., Thorpe, A. K., Thompson, D. R., Jongaramrungruang, S., Frankenberg, C., and Joshi, S. C.: Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior, IEEE T. Geosci. Remote, 58, 6480–6492, https://doi.org/10.1109/tgrs.2020.2976888, 2020. a, b
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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.
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