Methane (CH4) is a prominent greenhouse gas responsible for about 20% of all atmospheric radiative forcing. As we notice trends in increasing global temperatures, understanding and detecting these emissions has become increasingly important. This requires the creation of robust greenhouse gas plume detectors. Previous work at the NASA Jet Propulsion Laboratory has shown Convolutional Neural Networks (CNN) to be an appropriate solution to map methane sources from future imaging spectrometer missions, such as Carbon Mapper. However, current models suffer from a high rate of false positives due to false enhancements in the detected images.We have compiled datasets from two Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) California campaigns. We then trained a GoogleNet CNN Classifier model on each campaign. The baseline current model uses a Unimodal column-wise matched filter (CMF). This results in a model known to be sensitive to false enhancements, such as water/water vapor, bright/dark surfaces, or confuser materials with similar absorption wavelengths to methane. We first note improvements between the Unimodal CMF model and a new Surface-Controlled CMF model, whose dataset matches that of the Unimodal CMF model, but removes enhancements not matching the absorption wavelength of methane. From this, we note minimal improvement (1% increase in F1 score). We then experiment with various auxiliary products measuring albedo (rgbmu, SWALB), vegetation (NDVI, ENDVI), and water (h2o, NDWI) indices designed to combat issues known to produce false enhancements. After training on these new input representations for both campaigns, we noticed a significant improvement in the multi-channel model’s results. We observe an increase in the F1 score for classifying positive tiles from 0.78 to 0.86 when trained using auxiliary albedo indices, showing promise for future use of auxiliary products in improving methane plume detectors.

The Surface Biology and Geology global imaging spectrometer is primarily designed to observe the chemical fingerprint of the Earth’s surface. However imaging spectroscopy across the visible to shortwave infrared (VSWIR) can also provide important atmospheric observations of methane point sources, highly concentrated emissions from energy, waste management and livestock operations. Relating these point-source observations to greenhouse gas inventories and coarser, regional methane observations from sensors like the European Space Agency (ESA) TROPOMI will contribute to reducing uncertainties in local, regional and global carbon budgets. We present the Multi-scale Methane Analytic Framework (M2AF) that facilitates disentangling confounding processes by streamlining analysis of cross-scale, multi-sensor methane observations across three key, overlapping spatial scales: 1) global to regional scale, 2) regional to local scale, and 3) facility (point source scale). M2AF is an information system that bridges methane research and applied science by integrating tiered observations of methane from surface measurements, airborne sensors and satellite. Reducing uncertainty in methane fluxes with multi-scale analyses can improve carbon accounting and attribution which is valuable to both formulation and verification of mitigation actions. M2AF lays the foundation for extending existing methane analysis systems beyond their current experimental states, reducing latency and cost of methane data analysis and improving accessibility by researchers and decision makers. M2AF leverages the NASA Methane Source Finder (MSF), the NASA Science Data Analytics Platform (SDAP), Amazon Web Services (AWS) and two supercomputers for fast, on-demand analytics of cross-scale, integrated, quality-controlled methane flux estimates.