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Mapping global methane emissions from space with deep learning

Methane is a potent greenhouse gas; over a 100-year timeframe, its warming potential is 30 times greater than that of carbon dioxide. In fact, it has driven approximately 25% of human-induced warming since the start of the industrial era. Because methane has a relatively short atmospheric lifespan, promptly reducing these emissions offers a critical "fast-action" pathway to mitigating global temperature rise.This urgency is reflected in the Global Methane Pledge, where over 125 countries have committed to a 30% emissions reduction by 2030. To hit these targets, we must empower stakeholders to track localized point sources (emissions occurring from a small spatial footprint on the order of a few tens of meters) across the waste, agriculture, and energy sectors. The most cost-effective strategies are to mitigate emissions from oil and gas infrastructure, agricultural facilities, and landfills.To track these emissions on a global scale, scientists increasingly rely on space-based imaging. A prime example is NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) instrument on the International Space Station. While originally designed to map mineral composition in arid regions, scientists at NASA’s Jet Propulsion Laboratory (JPL) and the broader scientific community have leveraged EMIT's advanced hyperspectral capabilities to detect methane emissions. By recording hundreds of distinct bands of light for every pixel, it allows researchers to "see" the unique chemical fingerprints of these otherwise invisible gases.Building on these investments, in “Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT”, published in Proceedings of the National Academy of Sciences (PNAS), we describe a new approach that turns raw satellite data into scalable mitigation action. Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) is a deep-learning framework that represents a significant step toward automating the detection, enhancement prediction, and source estimation of methane plumes globally. We demonstrate how MAPL-EMIT achieves a high recall of 84% on expert annotated plumes and has a high signal to noise ratio compared to existing matched-filter-based enhancement methods. To support the broader scientific community, we're releasing our global plume database on Earth Engine along with the trained model and synthetic plumes on Kaggle and an inference library on Github.

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