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MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery

Jongaramrungruang, Siraput and Thorpe, Andrew K. and Matheou, Georgios and Frankenberg, Christian (2022) MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery. Remote Sensing of Environment, 269 . Art. No. 112809. ISSN 0034-4257. doi:10.1016/j.rse.2021.112809. https://resolver.caltech.edu/CaltechAUTHORS:20211207-393219000

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Abstract

Methane is one of the most important anthropogenic greenhouse gases with a significant impact on the Earth's radiation budget and tropospheric background ozone. Despite a well-constrained global budget, quantification of local and regional methane emissions has proven challenging. Recent advancements in airborne remote sensing instruments such as from the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) provide 2-D observations of CH₄ plume column enhancements at an unprecedented resolution of 1–5 m over large geographic areas. Quantifying an emission rate from observed plumes is a critical step for understanding local emission distributions and prioritizing mitigation efforts. However, there exists no method that can predict emission rates from detected plumes in real-time without ancillary data reliably. In order to predict methane point-source emissions directly from high resolution 2-D plume images without relying on other local measurements such as background wind speeds, we trained a convolutional neural network model called MethaNet. The training data was derived from large eddy simulations of methane plumes and realistic measurement noise over agricultural, desert and urban environments. Our model has a mean absolute percentage error for predicting unseen plumes under 17%, a significant improvement from previous methods that require wind information. Using MethaNet, a validation against a natural gas controlled-release experiment agrees to within the precision error estimate. Our results support the basis for the applicability of using deep learning techniques to quantify CH₄ point sources in an automated manner over large geographical areas, not only for present and future airborne field campaigns but also for upcoming space-based observations in this decade.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.rse.2021.112809DOIArticle
ORCID:
AuthorORCID
Jongaramrungruang, Siraput0000-0002-2477-2043
Thorpe, Andrew K.0000-0001-7968-5433
Matheou, Georgios0000-0003-4024-4571
Frankenberg, Christian0000-0002-0546-5857
Additional Information:© 2021 Elsevier. Received 12 March 2021, Revised 1 October 2021, Accepted 14 November 2021, Available online 4 December 2021. This work is part of SJ's NASA Earth and Space Science Fellowship (NESSF, grant no. 80NSSC18K1350). We acknowledge the Resnick Sustainability Institute at Caltech for their kind support with computing resources. This work was supported in part by NASA's Carbon Monitoring System (CMS) Prototype Methane Monitoring System for California. We thank the AVIRIS-NG team and colleagues at the Pacific Gas and Electric Company for their support for controlled-release experiments. Description of author's responsibilities. SJ designed the research objectives, performed the analysis, and wrote the paper. GM ran the LES model, and provided output. AT provided the AVIRIS-NG datasets and supported the writing. CF provided guidance and support on scientific approaches and experimental setups, and advised the writing of this paper. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Group:Resnick Sustainability Institute
Funders:
Funding AgencyGrant Number
NASA Earth and Space Science Fellowship80NSSC18K1350
Resnick Sustainability InstituteUNSPECIFIED
Subject Keywords:Methane gas; Methane detection; Methane quantification; Deep learning; Point-source emission; Regional budget; AVIRIS-NG; Greenhouse gas; LES; CNN
DOI:10.1016/j.rse.2021.112809
Record Number:CaltechAUTHORS:20211207-393219000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20211207-393219000
Official Citation:Siraput Jongaramrungruang, Andrew K. Thorpe, Georgios Matheou, Christian Frankenberg, MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery, Remote Sensing of Environment, Volume 269, 2022, 112809, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2021.112809.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112248
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Dec 2021 23:46
Last Modified:01 Feb 2022 22:58

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