The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near-term climate change. Emission inventories generated through surface activity measurements are reported annually or bi-annually and evaluated periodically through a “Global Stocktake”. Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (smoothing error), as-well-as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite-based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, -11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25 to 1.3 Tg CH4/ yr / yr), primarily in the Indo-Pakistan region, sub-tropical Africa, and the Brazilian arc of deforestation; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.

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.

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.