24 Nov 2022
 | 24 Nov 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

Methane retrieval from airborne HySpex observations in the short-wave infrared

Philipp Hochstaffl, Franz Schreier, Claas Henning Köhler, Andreas Baumgartner, and Daniele Cerra

Abstract. A reduction of methane emissions could help to mitigate global warming on a relatively short time scale. Monitoring of local and regional anthropogenic CH4 emissions is crucial in order to increase our understanding of the methane budget which is still subject to scientific debate.

The study compares various retrieval schemes that estimate localized CH4 emissions from ventilation shafts in the Upper Silesian Coal Basin (USCB) in Poland using short-wave infrared nadir observations of the airborne imaging spectrometer HySpex. The examined methods are divided into nonlinear and linear schemes. The former class are of iterative nature and encompass various nonlinear least squares setups while the latter are represented by the Matched Filter (MF), Singular Value Decomposition (SVD) and Spectral Signature Detection (SSD) algorithms. Particular emphasis is put on strategies to rem- edy the problem of albedo related biases due to correlation with broad band absorption features caused by the hyperspectral instrument's low spectral resolution.

It was found that classical nonlinear least squares fits based on the Beer InfraRed Retrieval Algorithm (BIRRA) suffers from surface-type dependent biases. The effect is more pronounced for retrievals from single spectral intervals but can be mitigated when multiple intervals are combined. The albedo related correlation is also found in the BIRRA solutions for the separable least squares. A new BIRRA setup that exploits the inverse of a scene's covariance structure to account for reflectivity statistics significantly reduces the albedo bias and enhances the CH4 signal so that the method infers two- to threefold higher methane concentrations.

The linear estimators turned out to be very fast and well suited to detect enhanced levels of methane. The linearized BIRRA forward model turned out to be sensitive to the selected retrieval interval and in the default setup only works for very narrow windows. Other well established linear methods such as the MF and SVD identified the methane pattern as well and largely agree with the BIRRA fitted enhancements hence the methods allow quantitative estimates of methane. The latter two methods yielded increased performance when the scene was further divided into clusters by applying k-means in a preprocessing step. Methane plumes detected with the simple SSD method were faint and found rather sensitive to the polynomial used to compute the method's residuum ratio.

Philipp Hochstaffl et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-271', David R. Thompson, 12 Dec 2022
    • AC1: 'Reply on RC1', Philipp Hochstaffl, 21 Apr 2023
  • RC2: 'Comment on amt-2022-271', Anonymous Referee #2, 15 Jan 2023
    • AC2: 'Reply on RC2', Philipp Hochstaffl, 21 Apr 2023
  • RC3: 'Comment on amt-2022-271', Anonymous Referee #3, 01 Feb 2023
    • AC3: 'Reply on RC3', Philipp Hochstaffl, 21 Apr 2023

Philipp Hochstaffl et al.

Philipp Hochstaffl et al.


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Short summary
The study examines the feasibility of methane retrievals from hyperspectral imaging observations using different retrieval schemes. One of the core challenges is the high spatial and moderate spectral resolution as it makes separation of spectral variations caused by molecular absorption and surface reflectivity challenging. It was found that localized methane enhancements can be detected and potentially quantified from HySpex airborne observations using various retrieval schemes.