Abstract
Despite methane’s important role as a greenhouse gas, the contribution
of individual sources to rising methane concentrations in Earth’s
atmosphere is poorly understood. This is in part due to the lack of
frequent measurements on a global scale, required to accurately quantify
fugitive methane sources. Future missions such as Earth Surface Mineral
Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and
Carbon Mapper promise to provide global, spatially resolved spectroscopy
observations that will allow us to map methane sources. However, the
detection and attribution of individual methane sources is challenged by
retrieval artifacts and noise in retrieved methane concentrations.
Additionally, manual methane plume detection is not scalable to global
space-borne observations due to the sheer volume of data generated. A
robust automated system to detect methane plumes is needed. We evaluated
the performance and sensitivity of several methane plume detection
methods on 30m to 60m hyperspectral imagery, downsampled from airborne
campaigns with AVIRIS-NG. To aid the training of the plume detection
models, we explored supplementing downsampled airborne imagery with
Large Eddy Simulations (LES) of methane plumes. We compared baseline
methods such as thresholding and random forest classifiers, as well as
state-of-the-art deep learning methods such as convolutional neural
networks (CNNs) for classification and conditional adversarial networks
(pix2pix) for plume segmentation.