Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning

TL;DR

This paper presents a deep learning method to automatically detect methane leaks in multispectral satellite data, significantly improving detection accuracy and enabling high-frequency global monitoring of emissions.

Contribution

It introduces a novel deep learning approach that automates methane leak detection in multispectral satellite data without prior leak site knowledge, reducing false positives.

Findings

Deep learning improves methane detection accuracy

Automated detection reduces false positives

Enables high-frequency global monitoring

Abstract

Methane is one of the most potent greenhouse gases, and its short atmospheric half-life makes it a prime target to rapidly curb global warming. However, current methane emission monitoring techniques primarily rely on approximate emission factors or self-reporting, which have been shown to often dramatically underestimate emissions. Although initially designed to monitor surface properties, satellite multispectral data has recently emerged as a powerful method to analyze atmospheric content. However, the spectral resolution of multispectral instruments is poor, and methane measurements are typically very noisy. Methane data products are also sensitive to absorption by the surface and other atmospheric gases (water vapor in particular) and therefore provide noisy maps of potential methane plumes, that typically require extensive human analysis. Here, we show that the image recognition capabilities of deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data, with dramatically reduced false positive rates compared with state-of-the-art multispectral methane data products, and without the need for a priori knowledge of potential leak sites. Our proposed approach paves the way for the automated, high-definition and high-frequency monitoring of point-source methane emissions across the world.