Mapping global methane emissions from space with deep

What happened

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.

In contrast, point source mappers like EMIT excel at measuring methane emissions at the facility scale. 4 nm spectral sampling), sufficient to capture the chemical signature of methane at a high signal to noise ratio. However, fully unlocking the potential of this rich data at a global scale presents additional challenges.

The Earth's varied landscapes provide a complex backdrop, and some surface materials can masquerade as methane, making the identification of smaller or more diffuse sources particularly challenging. To build on the EMIT team's foundational work and enable high-throughput global mapping, we collaborate with them to apply deep-learning models that can understand the broader visual context of the scene.

This work aligns with Google’s broader effort behind Google Earth AI, our collection of geospatial models and datasets to turn planetary data into actionable intelligence. By applying deep learning to satellite imagery at scale, we aim to complement broader planetary AI initiatives with specialized tools for targeted environmental monitoring. We built MAPL-EMIT using an end-to-end vision transformer architecture (Swin-S transformer).

While many approaches analyze hyperspectral data on a pixel-by-pixel basis, MAPL-EMIT leverages modern computer vision techniques to process the complete spectrum of light alongside its surrounding spatial context.

Why it matters

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. Measuring methane from space requires balancing three key factors: (1) field of view (spatial coverage/revisit), (2) spatial resolution, and (3) spectral resolution. 1 nm).

What to watch

By analyzing how gas disperses across the landscape, the model is better equipped to distinguish a true, wind-blown methane plume (a trail of methane gas dispersing from a specific source) from a patch of ground that simply shares a similar spectral signature, which has historically caused false methane detections. Crucially, this spatial awareness empowers the model to untangle highly complex scenes.

In dense industrial regions, emissions from multiple neighboring facilities often merge into a single cloud. To make sense of these scenarios, MAPL-EMIT simultaneously solves three distinct tasks: Transformer-based models require massive amounts of data to learn, but a global, labeled dataset of millions of real-world methane emissions simply doesn't exist. To overcome this, we developed a physics-based simulation framework. 6 million synthetic methane plumes and injected them directly into real EMIT scenes.