Investigation of Spectral Bands and Sensor Parameters for Methane Emission Detection Imaging Spectrometer

Recently, interest has increased in developing remote sensing (RS) sensor systems with a high spatial and spectral resolution for quantifying methane (CH4) emissions from point sources. Evaluating sensor parameters can lead to a better understanding of technological advancements in CH4 emission estimation. This study addresses several important topics, including the robust algorithm for point source CH4 emission detection, selecting optimum CH4 absorption bands and their sensitivity analysis, and evaluating sensor parameters such as spatial and spectral resolution and signal-to-noise ratio (SNR). Since the matched filter algorithm efficiently detects point source emissions with palpable accuracy, it has been used here for the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor data acquired over the USA. The optimum band selection for CH4 detection was determined using a normalized sensitivity method to investigate the effects of aerosol, water vapor (WV), and surface albedo variations. The impact of these parameters on the CH4 absorption bands in the short-wave infrared (SWIR) range has been analyzed using MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer (RT) model simulations. Using AVIRIS-NG data as a testbed, various methods have been used to reconstruct original data and generate data sets with different sensor specifications. The effects of the sensor parameters, such as spatial and spectral resolution and SNR, on the detection accuracy were analyzed. The results show the significance of SNR over the spatial and spectral resolution for better emission detection. It indicates the reasonable selection of sensor parameters that can help to create an optimal sensor system that will ultimately support the development of an imaging spectrometer suitable for CH4 emission estimation.