Researchers in Britain have built an automated tool that detects methane clouds from space using machine learning on hyperspectral data. This breakthrough could pinpoint methane super emitters and accelerate actions to curb these potent greenhouse gas sources.
While climate strategies often prioritize carbon dioxide, cutting methane is crucial to slowing the pace of warming. Methane traps heat at a rate far higher than CO2 over the short term, yet it lingers in the atmosphere for only about seven to 12 years, compared with centuries for CO2. Targeted methane reductions can yield meaningful gains in global temperature control and air quality, offering relief for communities in both Canada and the United States where emissions come from oil, gas, and other industries.
Swift action to reduce methane from human activities is projected to slow warming directly and improve air quality. Researchers estimate feasible methane reductions could prevent roughly 0.3 degrees Celsius of warming over the next two decades, a meaningful margin for North American climate resilience.
The system arises from a collaboration involving Oxford University, reflecting a growing European-US partnership in satellite-enabled environmental intelligence. The breakthrough addresses a long-standing challenge: mapping methane plumes from aerial imagery quickly and accurately. Methane gas is transparent to visible light and to many spectral ranges used by satellites, so detecting it requires specialized sensing and careful data processing. Noise and manual methods had previously slowed plume identification.
The new system is far more efficient
A pioneering machine learning tool developed by researchers from Oxford in the United Kingdom overcomes these hurdles by spotting methane clouds in hyperspectral satellite data. It works by detecting narrower spectral bands than common multispectral satellites, which helps tune the methane signature and filter out noise. The tradeoff is the sheer volume of data, which makes processing rely on artificial intelligence to handle the load.
Researchers validated the model with 167,825 hyperspectral mosaics, each covering about 1.64 square kilometers, captured by NASA’s AVIRIS sensor over the Four Corners region of the United States. The algorithm was then applied to data from additional hyperspectral satellites in orbit.
On average the model achieves over 81 percent accuracy in identifying large methane plumes and is about 21.5 percent more accurate than the most detailed systems previously available. It also delivers a notably lower false positive rate, cutting such errors by roughly 41.83 percent compared with earlier methods.
To encourage ongoing methane research, the annotated dataset and the code used for the model have been released on the project page. The team is exploring whether the model can run directly on satellites, enabling other satellites to make follow-up observations as part of the NIO.space initiative. The goal is to support faster, coordinated sensing across multiple satellites in space-based networks.
Lead researcher Vít Růžička, a PhD student in the Department of Computer Science at the University of Oxford, explained that the process could start with priority alerts on Earth. These alerts would include coordinates of identified methane sources, allowing a constellation of satellites to cooperate autonomously. The initial weak detection could serve as a warning to focus additional imagery on the location for follow-up confirmation and action.
The work demonstrates a scalable approach to monitoring methane emissions with hyperspectral data and AI, aligning with efforts to improve air quality and mitigate climate impacts. The methodology also has implications for other atmospheric gas detections and satellite-enabled environmental surveillance.