Hyperspectral SWIR imager optimization for optimal methane detection

Methane is a greenhouse gas that has a global warming potential (GWP) of 25 relative to carbon dioxide (CO2. Emission consists of anthropogenic and naturally occurring sources with natural emissions accounting for 35-50\% of total emissions. Effective mapping of natural emissions requires high field-of-view (FOV) global satellite coverage. Detection of small weak sources and mapping of larger plume nonuniformity requires a small ground-sample-distance (GSD). In this paper we design an imaging SWIR spectrometer for single pixel methane quantification using a tradeoff study between spectral resolution and sensor Signal-to-Noise Ratio (SNR). Increases in spectral resolution increase spectral separability of methane from other gases, while decreasing SNR. Increases in GSD increase SNR and FOV, while reducing spatial resolution. Retrieval performance was tested with simulated radiance spectra using vegetation surfaces, atmospheres with varying water and methane concentrations, and the developed sensor model. Gas concentrations were retrieved by finding the atmosphere providing the smoothest retrieved reflectance. Retrieval errors were studied to find the optimal sensor parameters.