Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer
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.
- Research Organization:
- Geolabe, Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Office of SBIR/STTR Programs (SBIR/STTR)
- Grant/Contract Number:
- SC0022398
- OSTI ID:
- 2332920
- Journal Information:
- Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 15; ISSN 2041-1723
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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