Reply on RC1

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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.