Operational machine learning for remote spectroscopic detection of CH$_{4}$ point sources

TL;DR

This paper presents the first operational deployment of machine learning for automated methane leak detection using spaceborne imaging spectrometers, significantly reducing false positives and enabling scalable global monitoring of methane emissions.

Contribution

The study introduces a large, diverse dataset and a novel ensembling approach that reduces false detections by over 74%, advancing operational AI-based methane detection from space.

Findings

Processed over 25,000 hyperspectral images in 11 months

Detected 2,851 methane leaks and issued 834 notifications

Reduced false detections by over 74% with model ensembling

Abstract

Mitigating anthropogenic methane sources is one of the most cost-effective levers to slow down global warming. While satellite-based imaging spectrometers, such as EMIT, PRISMA, and EnMAP, can detect these point sources, current methane retrieval methods based on matched filters produce a high number of false detections requiring manual verification. To address this challenge, we deployed a ML system for detecting methane emissions within the Methane Alert and Response System (MARS) of UNEP's IMEO. This represents the first operational deployment of automated methane point-source detection using spaceborne imaging spectrometers, providing regular global coverage and scalability to future constellations with even higher data volumes. This task required several technical advances. First, we created one of the largest and most diverse and global ML ready datasets to date of annotated methane plumes from three imaging spectrometer missions, and quantitatively compared different deep learning model configurations. Second, we extended prior evaluation methodologies from small, tiled datasets to full granules that are more representative of operational use. This revealed that deep learning models still produce a large number of false detections, a problem we addressed with model ensembling, which reduced false detections by over 74%. During 11 months of operational deployment, our system processed more than 25,000 hyperspectral products faciliting the verification of 2,851 distinct methane leaks, which resulted in 834 stakeholder notifications. We further demonstrate the model's utility in verifying mitigation success through case studies in Libya, Argentina, Oman, and Azerbaijan. Our work represents a critical step towards a global AI-assisted methane leak detection system, which is required to process the dramatically higher data volumes expected from current and future imaging spectrometers.