Mapping methane concentrations from a controlled release experiment using the next generation airborne visible/infrared imaging spectrometer (AVIRIS-NG)

Abstract

Emissions estimates of anthropogenic methane (CH4) sources are highly uncertain and many sources related to energy production are localized yet difficult to quantify.Airborne imaging spectrometers like the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) are well suited for locating CH4 point sources due to their ability to map concentrations over large regions with the high spatial resolution necessary to resolve localized emissions. AVIRIS-NG was deployed during a field campaign to measure controlled CH4 releases at the Rocky Mountain Oilfield Testing Center (RMOTC) in Wyoming, U.S. for multiple flux rates and flight altitudes. Two algorithms were applied to AVIRIS-NG scenes, a matched filter detection algorithm and a hybrid retrieval approach using the Iterative Maximum a Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm and Singular Value Decomposition.Plumes for releases as low as 14.16 m 3 /h (0.09 kt/year) were consistently observed by AVIRIS-NG at multiple flight altitudes and images of plumes were in agreement with wind directions measured at ground stations. In some cases plumes as low as 3.40 m 3 /h (0.02 kt/year) were detected, indicating that AVIRIS-NG has the capability of detecting a wide range of fugitive CH4 source categories for natural gas fields. This controlled release experiment is the first of its kind using AVIRIS-NG and demonstrates the utility of imaging spectrometers for direct attribution of emissions to individual point source locations. This is particularly useful given the large uncertainties associated with anthropogenic CH4 emissions, including those from industry, gas transmission lines, and the oil and gas sectors.

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

“…Prior studies of CH 4 with the "Classic" AVIRIS instrument by Thorpe et al (2014) detected local enhancements of 1 ppm within a kilometer-thick atmospheric model layer. Later studies by Thorpe et al (2015) using AVIRIS-NG found similar enhancements in the distal regions of plumes associated with CH 4 fluxes of 14.2 m 3 h −1 (500 standard cubic feet per hour, scfh) under moderate (5 m s −1 ) winds. Resolving plumes of this magnitude under similar conditions should be possible with a sensitivity of 1000 ppm m. Better performance would further reduce ambiguity and improve the detail of diffuse plumes.…”

Satellite observations of atmospheric methane and their value for quantifying methane emissions

“…Bovensmann et al (2010) did not include transport error in their analysis which may have led to overoptimistic results. With 2 × 2 km 2 pixel resolution, CarbonSat would be limited in its ability to resolve the structure of individual methane plumes since airborne mapping shows plumes to be smaller in scale even for large point sources Thorpe et al, 2016;Frankenberg et al, 2016). The 0.05 × 0.05 km 2 resolution of GHGSat, with imaging over a 12 × 12 km 2 grid, has better potential for resolving the plume structure.…”

“…The methane airborne mapper (MAMAP) retrieves methane in the SWIR at 1.6 µm, similar to SCIAMACHY, but currently lacks imaging capabilities. Imaging spectrometers initially designed for surface remote sensing have been shown to detect methane plumes with horizontal resolution as fine as 1 m either in the SWIR using the strong 2.3 µm band (Roberts et al, 2010;Thorpe et al, 2016) or in the TIR (Tratt et al, 2014;Hulley et al, 2016). These imaging spectrometers such as AVIRIS-NG (SWIR) and MAKO or HyTES (TIR) have much coarser spectral resolution than MAMAP or current satellite instruments (e.g., 5 nm for AVIRIS-NG).…”

“…However, at this fine spatial resolution, concentration enhancements over point sources are much higher and can be discerned down to a detection threshold of only 2 kg h −1 . A major advantage is that the fine structure of the plume shape can be observed, allowing for localized source attribution (Thompson et al, 2015;Thorpe et al, 2016).…”

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

“…The AVIRIS-C sensor images spectral radiance in 224 bands from 370 nm (visible) to 2500 nm (shortwave infrared, SWIR) with 10 nm sampling and SNR of >1000:1 at 600 nm and >400:1 at 2200 nm [28,29]. There were twelve, ~12 km swath flight runs per season that provided 20% image overlap among runs.…”