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Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer

Author

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  • Bertrand Rouet-Leduc

    (Disaster Prevention Research Institute
    Geolabe)

  • Claudia Hulbert

    (Geolabe)

Abstract

Curbing methane emissions is among the most effective actions that can be taken to slow down global warming. However, monitoring emissions remains challenging, as detection methods have a limited quantification completeness due to trade-offs that have to be made between coverage, resolution, and detection accuracy. Here we show that deep learning can overcome the trade-off in terms of spectral resolution that comes with multi-spectral satellite data, resulting in a methane detection tool with global coverage and high temporal and spatial resolution. We compare our detections with airborne methane measurement campaigns, which suggests that our method can detect methane point sources in Sentinel-2 data down to plumes of 0.01 km2, corresponding to 200 to 300 kg CH4 h−1 sources. Our model shows an order of magnitude improvement over the state-of-the-art, providing a significant step towards the automated, high resolution detection of methane emissions at a global scale, every few days.

Suggested Citation

  • Bertrand Rouet-Leduc & Claudia Hulbert, 2024. "Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47754-y
    DOI: 10.1038/s41467-024-47754-y
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    References listed on IDEAS

    as
    1. Andrea Licciardi & Quentin Bletery & Bertrand Rouet-Leduc & Jean-Paul Ampuero & Kévin Juhel, 2022. "Instantaneous tracking of earthquake growth with elastogravity signals," Nature, Nature, vol. 606(7913), pages 319-324, June.
    2. Bertrand Rouet-Leduc & Romain Jolivet & Manon Dalaison & Paul A. Johnson & Claudia Hulbert, 2021. "Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Daniel Zavala-Araiza & Ramón A Alvarez & David R. Lyon & David T. Allen & Anthony J. Marchese & Daniel J. Zimmerle & Steven P. Hamburg, 2017. "Super-emitters in natural gas infrastructure are caused by abnormal process conditions," Nature Communications, Nature, vol. 8(1), pages 1-10, April.
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