Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
- 1Google Research, Google
- 2Division of Biosphere Sciences and Engineering, Carnegie Institution for Science
- 3Jet Propulsion Laboratory, California Institute of Technology
Abstract
) 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.
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BibTeX
@article{batchu2026global,
title = {{Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT}},
author = {Vishal Batchu and Michelangelo Conserva and Alex Wilson and Anna M. Michalak and Varun Gulshan and Philip Gregory Brodrick and Andrew Thorpe and Christopher Van Arsdale},
journal = {Proceedings of the National Academy of Sciences},
year = {2026},
month = sep,
doi = {10.1073/pnas.2612145123},
url = {https://doi.org/10.1073/pnas.2612145123},
note = {CC BY},
}
RIS
TY - JOUR
TI - Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
AU - Vishal Batchu
AU - Michelangelo Conserva
AU - Alex Wilson
AU - Anna M. Michalak
AU - Varun Gulshan
AU - Philip Gregory Brodrick
AU - Andrew Thorpe
AU - Christopher Van Arsdale
PY - 2026
DA - 2026/09/01
JO - Proceedings of the National Academy of Sciences
DO - 10.1073/pnas.2612145123
UR - https://doi.org/10.1073/pnas.2612145123
AB - ) 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.
SN - 0027-8424
SN - 1091-6490
ER -