Abstract

Abstract. Methane is the second most important anthropogenic greenhouse gas in the Earth's atmosphere. To effectively reduce these emissions, a good knowledge of source locations and strengths is required. 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. In this study, we successfully applied the weighting function modified differential optical absorption spectroscopy (WFM-DOAS) algorithm to AVIRIS-NG data measured in Canada and the Four Corners region. The WFM-DOAS retrieval is conceptually located between the statistical matched filter (MF) and the optimal-estimation-based iterative maximum a posteriori DOAS (IMAP-DOAS) retrieval algorithm, both of which were already applied successfully to AVIRIS-NG data. The WFM-DOAS algorithm is based on a first order Taylor series approximation of the Lambert–Beer law using only one precalculated radiative transfer calculation per scene. This yields the fast quantitative processing of large data sets. We detected several methane plumes in the AVIRIS-NG images recorded during the Arctic-Boreal Vulnerability Experiment (ABoVE) Airborne Campaign and successfully retrieved a coal mine ventilation shaft plume observed during the Four Corners measurement campaign. The comparison between IMAP-DOAS, MF, and WFM-DOAS showed good agreement for the coal mine ventilation shaft plume. An additional comparison between MF and WFM-DOAS for a subset of plumes showed good agreement for one plume and some differences for the others. For five plumes, the emissions were estimated using a simple cross-sectional flux method. The retrieved fluxes originated from well pads, cold vents, and a coal mine ventilation shaft and ranged between (155 ± 71) kg (CH4) h−1 and (1220 ± 450) kg (CH4) h−1. The wind velocity was a significant source of uncertainty in all plumes, followed by the single pixel retrieval noise and the uncertainty due to atmospheric variability. 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.

Highlights

  • Methane (CH4) is an important greenhouse gas with a global warming potential approximately 28 times larger than that of carbon dioxide (CO2) on a timescale of 100 years (IPCC, 2013)

  • AVIRIS-NG is a hyperspectral imaging spectrometer with a spectral sampling of ∼ 5 nm and a spectral resolution of ∼ 5–6 nm depending on the wavelength (Hamlin et al, 2011; Chapman et al, 2019)

  • The preselection contained 13 flight lines on 5 different days in August 2017 covering different types of sources and surface types. To include another strong source under different observation conditions, we included a coal mine ventilation shaft plume observed during the Four Corners measurement campaign in 2015 (Frankenberg et al, 2016)

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Summary

Introduction

Methane (CH4) is an important greenhouse gas with a global warming potential approximately 28 times larger than that of carbon dioxide (CO2) on a timescale of 100 years (IPCC, 2013). Successful algorithms for the retrieval of methane comprised either a matched filter approach (MF; Thompson et al, 2015), which uses a hypothesis test between presence and absence of additional CH4 to infer CH4 increases, or an adaption of the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) retrieval (Frankenberg et al, 2005; Thorpe et al, 2013, 2017; Cusworth et al, 2019) to AVIRIS-NG airborne data, which is an iterative optimal-estimation-based algorithm The latter is computationally very expensive which makes it less suited for analyzing large data sets acquired during longer measurement campaigns (Thorpe et al, 2017).

The AVIRIS-NG instrument and measurements
Meteorological data from ERA5 and weather stations
Adaption of WFM-DOAS algorithm to AVIRIS-NG measurements
Retrieval of total column increases with WFM-DOAS
Comparison of major fitting windows in the SWIR spectral range
Sensitivity analysis
Filtering of poor fits
Detection of plumes
Comparison of WFM-DOAS retrieval results with IMAP-DOAS and MF results
Flux and uncertainty estimation based on cross-sectional flux method
Findings
Discussion
Summary and conclusions
Full Text
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