Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT.
- DOI
- 10.1073/pnas.2612145123
- Published
- 2026 Sep 8
- Container
- Proceedings of the National Academy of Sciences of the United States of America
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1073/pnas.2612145123,
title = {Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT.},
author = {Batchu VV and Conserva M and Wilson A and Michalak AM and Gulshan V and Brodrick PG and Thorpe AK and Arsdale CV},
year = {2026},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
doi = {10.1073/pnas.2612145123},
url = {https://doi.org/10.1073/pnas.2612145123}
}RIS
TY - JOUR TI - Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT. AU - Batchu VV AU - Conserva M AU - Wilson A AU - Michalak AM AU - Gulshan V AU - Brodrick PG AU - Thorpe AK AU - Arsdale CV PY - 2026 JO - Proceedings of the National Academy of Sciences of the United States of America DO - 10.1073/pnas.2612145123 UR - https://doi.org/10.1073/pnas.2612145123 ER -
APA
VV, B., M, C., A, W., AM, M., V, G., PG, B., AK, T., & CV, A. (2026). Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT.. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.2612145123
Source records
- pubmed · retrieved 2026-11-10T10:44:03.833Z
- europe-pmc · retrieved 2026-11-10T10:44:03.827Z