  The strong radiative forcing by atmospheric methane has stimulated interest
in identifying natural and anthropogenic sources of this potent greenhouse gas.
Point sources are important targets for quantification, and anthropogenic
targets have potential for emissions reduction. Methane point source plume
detection and concentration retrieval have been previously demonstrated using
data from the Airborne Visible InfraRed Imaging Spectrometer Next Generation
(AVIRIS-NG). Current quantitative methods have tradeoffs between computational
requirements and retrieval accuracy, creating obstacles for processing
real-time data or large datasets from flight campaigns. We present a new
computationally efficient algorithm that applies sparsity and an albedo
correction to matched filter retrieval of trace gas concentration-pathlength.
The new algorithm was tested using AVIRIS-NG data acquired over several point
source plumes in Ahmedabad, India. The algorithm was validated using simulated
AVIRIS-NG data including synthetic plumes of known methane concentration.
Sparsity and albedo correction together reduced the root mean squared error of
retrieved methane concentration-pathlength enhancement by 60.7% compared with a
previous robust matched filter method. Background noise was reduced by a factor
of 2.64. The new algorithm was able to process the entire 300 flightline 2016
AVIRIS-NG India campaign in just over 8 hours on a desktop computer with GPU
acceleration.
