Identification of false methane plumes for orbital imaging spectrometers: A case study with EMIT

Orbital imaging spectroscopy in the shortwave infrared is a powerful measurement technique that can lead to the detection and monitoring of both anthropogenic and natural methane point sources. Analysts map the absorption of methane by applying spectroscopic detection methods like the matched filter (MF) or Differential Optical Absorption Spectroscopy (DOAS) at each pixel in a scene. Unfortunately, methane plume identification is complicated by false detections due to instrument noise, especially for subtle methane enhancements at low signal levels, and surface spectroscopy features that mimic gas methane absorption features. Current systems rely on manual review to vet these detections. This review process is time consuming and can be subjective for plumes close to the instrument detection limit. As observations scale up from proof of concept studies with hundreds of plumes to global monitoring systems having thousands, improved automation will be necessary to reduce the cost and subjectivity of manual review. This paper presents a new purely spectroscopic approach for rejecting false positives based on the aggregate plume transmittance. Specifically, we use the total plume transmittance as a discriminator with improved model properties and noise statistics that can dramatically improve false positive rejection rates. A sampling approach provides rigorous detection p-values that account for spatial correlations in background interference. We demonstrate the approach on a catalog of thousands of plumes from the EMIT (Earth Surface Mineral Dust Source Investigation) imaging spectrometer, and illustrate its use in the context of a methane plume mapping workflow. This process identifies 92 potential errors in the existing review process: 42 likely false positives and 50 false negatives. Including spectral information during the plume review process can disambiguate marginal detections, making methane point source observing campaigns more robust and sensitive than a review process based primarily on plume morphology. • Novel spectroscopic approach vets methane point sources using plume transmittance. • Integrates spectral shape-fitting, spatial averaging, and background sampling. • Identifies potential false positives and negatives on EMIT plume catalogue. • Quantitative algorithm reduces labor intensity and subjectivity in manual reviews. • Improves accuracy and efficiency of plume verification, driving future automation.