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.