Comparison of Methane Detection Using Shortwave and Longwave Infrared Hyperspectral Sensors under Varying Environmental Conditions

Abstract

Methane is a prevalent greenhouse gas with potent heat trapping capabilities, but methane emissions can be difficult to detect. Hyperspectral imagery is an effective method of detection which can be used to locate methane emission sources, as well as provide accountability for reaching emissions reduction goals. Because of methane's absorption features, both shortwave infrared (SWIR) and longwave infrared (LWIR) hyperspectral sensors have been used to accurately detect methane plumes. However, surface, environmental, and atmospheric background conditions can cause methane detectability to vary, and there have not been previous studies which evaluate this variability over a wide range of conditions. To assess this variation, this trade study compared methane detectability for two airborne hyperspectral sensors: AVIRIS-NG in the SWIR and HyTES in the LWIR. We modeled methane plume detection under a wide range of precisely known conditions by making use of synthetic images which were comprised of MODTRAN-generated radiance curves. We applied a spectral matched filter to these images to assess detection accuracy, and used these results to identify the conditions which have the most significant impact on detectability in the SWIR and LWIR. We then computed the specific boundaries on these conditions which make methane most detectable for each instrument; these novel results explore methane detectability over a broader range of conditions and sensors than previous studies. This trade study and methodology can aid decision-making about which sensors are most useful for various types of methane emission analysis, such as leak detection and emission rate quantification.