Sensitivity and Uncertainty in Matched-Filter-Based Gas Detection with Imaging Spectroscopy

Recent advances in remote imaging spectroscopy have increased their utility for detecting and quantifying green- house gas emissions. In fact, multiple airborne [1] and space- based [2], [3], [4], [5] instruments are actively used to estimate methane emissions. Many of these measurements are made using matched-filter-based detection and estimation algorithms. In this work, we present new methods for quantifying the accuracy and uncertainty of these algorithms. Two new metrics are proposed that capture the biases and uncertainties in gas quantity measurements stemming from local surface and atmospheric variation, observation and solar geometries, and sensor noise. These metrics are demonstrated using data from the Earth Surface Mineral Dust Source Investigation (EMIT) [6] imaging spectrometer currently collecting Earth observations onboard the International Space Station.