Rouet-Leduc Bertrand, Hulbert Claudia
Disaster Prevention Research Institute, Kyoto University, Japan.
Geolabe, Los Alamos, NM, USA.
Nat Commun. 2024 May 14;15(1):3801. doi: 10.1038/s41467-024-47754-y.
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 km, corresponding to 200 to 300 kg CH h 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.
控制甲烷排放是减缓全球变暖最有效的行动之一。然而，监测排放仍然具有挑战性，因为由于在覆盖范围、分辨率和检测精度之间必须做出权衡，检测方法的定量完整性有限。在这里，我们表明深度学习可以克服多光谱卫星数据所带来的光谱分辨率方面的权衡，从而产生一种具有全球覆盖范围以及高时间和空间分辨率的甲烷检测工具。我们将我们的检测结果与机载甲烷测量活动进行了比较，这表明我们的方法能够在哨兵 -2 数据中检测到甲烷点源，直至 0.01 千米的羽流，对应于 200 至 300 千克 CH₄/小时的源。我们的模型相对于现有技术有一个数量级的提升，朝着每隔几天在全球范围内自动、高分辨率检测甲烷排放迈出了重要一步。
