Mapping global methane emissions from space with deep learning

The challenge of spotting methane from space

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

Global mappers like TROPOMI were designed to detect small changes in background methane concentrations by integrating high coverage (approximately 2,600 km swath width), coarse spatial resolution (around 5.5 km x 3.5 km), and fine spectral sampling (0.1 nm).

In contrast, point source mappers like EMIT excel at measuring methane emissions at the facility scale. They achieve this by combining moderate coverage (an 80 km wide field of view) with very high spatial resolution (60 meters) and a moderate spectral resolution (7.4 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.