To address discrepancies between bottom-up and top-down inventories of methane emissions, the detection and quantification of methane point source emissions is of critical concern. Multiple airborne imaging spectrometer campaigns have identified the heavy-tailed distribution of point source methane emissions. The quantification of point source plumes is a two-part problem requiring the detection and delineation of plumes, followed by an accurate accounting of their total methane enhancement. Plume detection and delineation has often relied on manual or statistical methods, but automated methods taking into account plume morphology are essential as the volume of imaging spectrometer data rapidly increases. Fully convolutional neural networks (FCNNs) represent a robust solution to this problem allowing for the detection and delineation of methane point source emissions with minimal analyst input. This work demonstrates the applicability of FCNNs for accurate quantification of methane point source emissions by training a model on data from a 2019 Permian Basin survey by the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG). FCNNs were trained using plumes that were manually interpreted from matched filter retrievals of methane enhancements. Our methodology was able to accurately detect and delineate methane plumes, and did so with fewer false positives than statistical methods. Given the anticipated satellite imaging spectrometer missions capable of global mapping of point sources, automated deep learning methods will be necessary to deal with methane plume detection in very large volumes of data.

Methane is an important greenhouse gas, and anthropogenic methane emissions from point sources are a frequent target for emissions reductions. Airborne imaging spectrometers measuring shortwave infrared radiance are becoming regular sources of data for methane point source plume detection and flux estimation. Accurate and efficient detection and delineation of methane plumes will play an essential role in quantifying point source fluxes. Methane plumes are highly variable in space and time, whereas surfaces that are typically “false positive” detections in methane enhancement retrievals are more regularly shaped and change on longer time scales. This work aims to take advantage of plume variability by applying a fully convolutional network (FCN) to detection and delineation of methane plumes within imaging spectrometer time series data. Using a time series of matched filter methane retrieval products derived from Airborne Visible and InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG) data, we demonstrate the ability of a FCN to classify methane plumes at each time step. Comparison with plume detection and delineation using conventional statistical methods demonstrates the value of this approach. Automated approaches incorporating deep learning will become increasingly important as future global satellite missions greatly increase the frequency at which methane point sources are imaged.