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Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale

Jongaramrungruang, Siraput and Matheou, Georgios and Thorpe, Andrew K. and Zeng, Zhao-Cheng and Frankenberg, Christian (2021) Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale. Atmospheric Measurement Techniques, 14 (12). pp. 7999-8017. ISSN 1867-8548. doi:10.5194/amt-14-7999-2021. https://resolver.caltech.edu/CaltechAUTHORS:20220114-12582000

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Abstract

Methane (CH₄) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Remote sensing observations enable the detection and quantification of local methane emissions across large geographical areas, which is a critical step for understanding local flux distributions and subsequently prioritizing mitigation strategies. Obtaining methane column concentration measurements with low noise and minimal surface interference has direct consequences for accurately determining the location and emission rates of methane sources. The quality of retrieved column enhancements depends on the choices of the instrument and retrieval parameters. Here, we studied the changes in precision error and bias as a result of different spectral resolutions, instrument optical performance, and detector exposure times by using a realistic instrument noise model. In addition, we formally analyzed the impact of spectrally complex surface albedo features on retrievals using the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm. We built an end-to-end modeling framework that can simulate observed radiances from reflected solar irradiance through a simulated CH₄ plume over several natural and artificial surfaces. Our analysis shows that complex surface features can alias into retrieved methane abundances, explaining the existence of retrieval biases in current airborne methane observations. The impact can be mitigated with higher spectral resolution and a larger polynomial degree to approximate surface albedo variations. Using a spectral resolution of 1.5 nm, an exposure time of 20 ms, and a polynomial degree of 25, a retrieval precision error below 0.007 mole m⁻² or 1.0 % of total atmospheric CH₄ column can be achieved for high albedo cases, while minimizing the bias due to surface interference such that the noise is uncorrelated among various surfaces. At coarser spectral resolutions, it becomes increasingly harder to separate complex surface albedo features from atmospheric absorption features. Our modeling framework provides the basis for assessing tradeoffs for future remote sensing instruments and algorithmic designs. For instance, we find that improving the spectral resolution beyond 0.2 nm would actually decrease the retrieval precision, as detector readout noise will play an increasing role. Our work contributes towards building an enhanced monitoring system that can measure CH₄ concentration fields to determine methane sources accurately and efficiently at scale.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.5194/amt-14-7999-2021DOIArticle
https://avirisng.jpl.nasa.gov/dataportal/Related ItemAVIRIS-NG data portal
ORCID:
AuthorORCID
Jongaramrungruang, Siraput0000-0002-2477-2043
Matheou, Georgios0000-0003-4024-4571
Thorpe, Andrew K.0000-0001-7968-5433
Zeng, Zhao-Cheng0000-0002-0008-6508
Frankenberg, Christian0000-0002-0546-5857
Additional Information:© Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 12 Jul 2021 – Discussion started: 20 Jul 2021 – Revised: 18 Oct 2021 – Accepted: 20 Oct 2021 – Published: 22 Dec 2021. This work is part of Siraput Jongaramrungruang'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. We deeply thank Rupesh Jeyaram, for his kind support with a radiative transfer open-source software (https://github.com/RadiativeTransfer, last access: 20 July 2021) and his help with making our computations run faster. This research has been supported by the Earth Sciences Division (grant no. 80NSSC18K1350). Author contributions. SJ and CF conceptualized and designed the research objectives. SJ performed the analysis and wrote the paper. GM ran the LES model and provided output. CF provided guidance on the overall design and support on scientific approaches and experimental setups. AKT and ZCZ provided feedback and suggestions on the figures and text. All co-authors contributed to the writing of this paper. Data availability. AVIRIS-NG data are publicly available via the AVIRIS-NG data portal by JPL via https://avirisng.jpl.nasa.gov/dataportal/ (JPL, 2021). Author contributions. SJ and CF conceptualized and designed the research objectives. SJ performed the analysis and wrote the paper. GM ran the LES model and provided output. CF provided guidance on the overall design and support on scientific approaches and experimental setups. AKT and ZCZ provided feedback and suggestions on the figures and text. All co-authors contributed to the writing of this paper. The contact author has declared that neither they nor their co-authors have any competing interests. Review statement. This paper was edited by Gerrit Kuhlmann and reviewed by two anonymous referees.
Group:Resnick Sustainability Institute
Funders:
Funding AgencyGrant Number
NASA80NSSC18K1350
Issue or Number:12
DOI:10.5194/amt-14-7999-2021
Record Number:CaltechAUTHORS:20220114-12582000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220114-12582000
Official Citation:Jongaramrungruang, S., Matheou, G., Thorpe, A. K., Zeng, Z.-C., and Frankenberg, C.: Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale, Atmos. Meas. Tech., 14, 7999–8017, https://doi.org/10.5194/amt-14-7999-2021, 2021.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112921
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:18 Jan 2022 16:11
Last Modified:18 Jan 2022 16:11

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