Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT.

Authors

Vishal V Batchu, Michelangelo Conserva, Alex Wilson, Anna M Michalak, Varun Gulshan, Philip G Brodrick, Andrew K Thorpe, Christopher V Arsdale

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 36. Pages e2612145123. Sep 08, 2026. Epub Sep 01, 2026.

Abstract

Anthropogenic methane (CH₄) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies. Space-based imaging spectroscopy is an emerging tool for identifying emissions globally, but existing approaches largely rely on manual plume identification. Here, we present the Methane Analysis and Plume Localization with Earth Surface Mineral Dust Source Investigation (MAPL-EMIT) model, an end-to-end vision transformer framework that leverages the complete radiance spectrum from the EMIT instrument to jointly retrieve methane enhancements across all pixels within a scene. This approach brings together spectral information with spatial context to significantly lower detection limits. MAPL-EMIT simultaneously supports enhancement quantification, plume delineation, and source localization, even for overlapping plumes. The model was trained on 3.6 million physics-based synthetic plumes injected into global EMIT radiance data. Synthetic evaluation confirms the model's ability to identify plumes with high recall and precision and to capture weaker plumes relative to existing matched-filter approaches. On real-world benchmarks, MAPL-EMIT captures 84% of known hand-annotated NASA EMIT-L2B plume complexes across a test set of 1,084 EMIT granules, while capturing roughly 1.5 times as many plausible plumes compared to human analysts. Further verification against coincident airborne data, top-emitting landfills, and controlled release experiments confirms the model's ability to identify previously uncaptured sources. By incorporating model-generated metrics such as spectral fit scores and estimated noise levels, the framework can limit false-positives. Overall, MAPL-EMIT enables high-throughput implementation on the full EMIT data catalog, shifting methane monitoring to a rapid, scalable paradigm for global plume mapping at the facility scale.

PMID:
42679027
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.

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