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\begin{document}

\title{Improving Imaging Spectrometer Methane Plume Detection with Large Eddy
Simulations}



\author[1,1]{Arjun Ashok Rao}%
\author[1,1]{Steffen Mauceri}%
\author[1,1]{Andrew Thorpe}%
\author[1,1]{Jake Lee}%
\author[2,2]{Siraput Jongaramrungruang}%
\author[3,3]{Riley Duren}%
\affil[1]{NASA Jet Propulsion Laboratory}%
\affil[2]{California Institute of Technology}%
\affil[3]{University of Arizona}%


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  \date{June 13, 2024}


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\begin{abstract}
Methane's high heat trapping potential has made it a priority for
quantification and mitigation efforts worldwide. Ground-based surveys
and in-situ measurement techniques to quantify natural and fugitive
methane emission sources are time-consuming, expensive, and often lead
to sparse measurements. Failure to accurately quantify emissions at the
point-source scale have thus led to poorly constrained emission
estimates. Airborne imaging spectrometers such as the Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and the Global
Airborne Observatory (GAO) have been employed to map the often
stochastic and intermittent point-source emissions from a diverse set of
source types including oil and gas, dairy, etc. A matched filter is
applied to the methane-absorption relevant spectral features of the
instrument's radiance cube. Machine learning models are then trained to
recognize methane plumes from these column-matched filter methane maps.
However, current Convolutional Neural Network (CNN) models suffer from a
high false-positive rate and poorly generalize to new scenes.
False-positive detections are primarily due to methane
absorption-mimicking surface spectroscopic features, as well as a lack
of training data. To supplement the available training data, we utilize
Large Eddy Simulations (LES) of methane point-source emissions to train
a Convolutional Neural Network (CNN) on a plume-classification task. We
observe a significant distribution shift between LES and AVIRIS-NG
plumes, primarily caused by high LES plume enhancements. Through a
series of image transforms verified through an adversarial approach
using a discriminator network, we minimize the distribution shift
between synthetic LES plumes and plumes observed by AVIRIS-NG and GAO.
CNNs trained on a mixture of LES and real-world plumes, and tested on
flightlines from multiple campaigns exhibit an error reduction compared
to previous models. The reduction in false-positive plume detections
demonstrates that supplementing the limited training data of real
methane plumes with LES provides an avenue to make automatic detection
more robust for future airborne and spaceborne missions such as SBG,
EMIT, and Carbon Mapper.%
\end{abstract}%



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