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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-275
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-275
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Aug 2020

27 Aug 2020

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This preprint is currently under review for the journal AMT.

Detection and Quantification of CH4 Plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data

Jakob Borchardt1, Konstantin Gerilowski1, Sven Krautwurst1, Heinrich Bovensmann1, Andrew Kenji Thorpe2, David Ray Thompson2, Christian Frankenberg3,2, Charles E. Miller2, Riley M. Duren4,2, and John Philip Burrows1 Jakob Borchardt et al.
  • 1Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 3California Institute of Technology, Division of Geological and Planetary Sciences, Pasadena, CA, USA
  • 4Institutes for Resilience, University of Arizona, Tuscon, AZ, USA

Abstract. Methane is the second most important anthropogenic greenhouse gas in the Earth's atmosphere. Reducing methane emissions is consequently an important element in limiting the global temperature increase below 2 °C compared to preindustrial times. Therefore, a good knowledge of source strengths and source locations is required. Anthropogenic methane emissions often originate from point sources or small areal sources, such as fugitive emissions at oil and gas production sites or landfills. Airborne remote sensing instruments such as the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) with meter scale imaging capabilities are able to yield information about the locations and magnitudes of methane sources, especially in areas with many potential emission sources.

To extract methane column enhancement information from spectra recorded with the AVIRIS-NG instrument, different retrieval algorithms have been used, e.g. the matched filter (MF) or the Iterative Maximum A Posteriori DOAS (IMAP-DOAS) retrieval. The WFM-DOAS algorithm, successfully applied to AVIRIS-NG data in this study, fills a gap between those retrieval approaches by being a fast, non-iterative algorithm based on a first order approximation of the Lambert-Beer law, which calculates the change in gas concentrations from deviations from one background radiative transfer calculation using precalculated weighting functions specific to the state of the atmosphere during the overflight. This allows the fast quantitative processing of large data sets. Although developed for high spectral resolution measurements from satellite instruments such as SCIAMACHY, TROPOMI and the MAMAP airborne sensor, the algorithm can be applied well to lower spectral resolution AVIRIS-NG measurements. The data set examined here was recorded in Canada over different gas and coal extraction sites as part of the larger Arctic Boreal Vulnerability Experiment (ABoVE) Airborne Campaign in 2017.

The noise of the retrieved CH4 imagery over bright surfaces (> 1 μW cm−2 nm−1 sr−1 at 2140 nm) was typically ±2.3 % of the background total column of CH4 when fitting strong absorption lines around 2300 nm, but could reach over ±5 % for darker surfaces (< 0.3 μW cm−2 nm−1 sr −1 at 2140 nm). Additionally, a worst case large scale bias due to the assumptions made in the WFM-DOAS retrieval was estimated to be ±5.4 %. Radiance and fit quality filters were implemented to exclude the most uncertain results from further analysis, mostly due to either dark surfaces or surfaces, where the surface spectral reflection structures are similar to CH4 absorption features at the spectral resolution of the AVIRIS-NG instrument.

We detected several methane plumes in the AVIRIS-NG images recorded during the ABoVE Airborne Campaign. For four of those plumes, the emissions were estimated using a simple cross sectional flux method. The retrieved fluxes originated from well pads and cold vents and ranged between (89 ± 46) kg (CH4) h−1 and (141 ± 87) kg (CH4) h−1. The wind uncertainty was a significant source of uncertainty for all plumes, followed by the single pixel retrieval noise and the uncertainty due to atmospheric variability. For one plume the wind was too low to estimate a trustworthy emission rate, although a plume was visible.

Jakob Borchardt et al.

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Jakob Borchardt et al.

Data sets

ABoVE: Hyperspectral Imagery from AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2018 Miller, C.E., R.O. Green, D.R. Thompson, A.K. Thorpe, M. Eastwood, I.B. Mccubbin, W. Olson-duvall, M. Bernas, C.M. Sarture, S. Nolte, L.M. Rios, M.A. Hernandez, B.D. Bue, and S.R. Lundeen https://doi.org/10.3334/ORNLDAAC/1569

Jakob Borchardt et al.

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Short summary
The AVIRIS-NG hyperspectral imager has been used successfully to identify and quantify anthropogenic methane sources utilizing different retrieval and inversion methods. In this paper, we examine the adaption and application of the WFM-DOAS retrieval algorithm to AVIRIS-NG measurements to retrieve local methane column enhancements. The uncertainties resulting from the retrieval method are quantified. Additionally, we estimate the emissions from four detected methane plumes.
The AVIRIS-NG hyperspectral imager has been used successfully to identify and quantify...
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