GHGSat (Canada)

GHGSat currently operates 14 methane satellites and has plans to expand the constellation further, acquiring images globally of facilities that could emit methane for monitoring and mitigation. The constellation produces almost 1,000 observations per day in which methane is detected, geolocated and quantified. It is impractical to rely on human inspection alone at such large scale, and so automated solutions are required. However, automation must handle a highly complex classification problem—distinguishing small methane plumes from retrieval artifacts—while operating reliably at very high throughput. Two common types of automation that help human operators are using machine learning models to detect methane and to propose segmentation masks. The first one helps reduce the total amount of data seen by human operators, and the second helps reduce the operator time spent per observation. While these types of automation are common in methane detection with coarse-resolution public satellites such as Sentinel-2 or EMIT, their applications to fine spectral and spatial resolution satellites have been more limited. To handle the growing amount of data, we develop transformer-based detection and segmentation models, which can assist operators in processing the observations. We present the models used and performance achieved in terms of precision and recall, both for detection and segmentation, as well as discuss future improvements to further diminish operator time.

Observational constraints on national scale methane emissions are needed to assist progress towards the goals of the Paris Agreements and the Global Methane Pledge. Here, we use 2023 blended TROPOMI+GOSAT observations of atmospheric methane in multiple analytical inversions to estimate emissions for all countries of the world at up to 25 km resolution. Prior emissions estimates are spatially distributed according to state-of-the-science bottom-up inventories, and country-level prior totals are adjusted by sector to match the emissions most recently reported to the UNFCCC. We enhance each inversion’s ability to capture point-source emissions not included in bottom-up inventories by redistributing oil-gas and coal emissions based on a gridded inventory constructed from GHGSat plume and null detections, and by enforcing native resolution emissions optimization at locations where plumes were observed by point source imagers including PRISMA, Sentinel-2, Landsat, EnMAP, GOES, and EMIT, and where large plumes were detected by TROPOMI. Our global total posterior emission of 562 Tg for 2023 is in line with previous coarse-scale global inversion studies. The inversions’ high resolution allows source separation and independent optimization of individual countries, confirmed by small posterior error correlations between countries. China (52.7 Tg), the U.S. (32.2 Tg), India (25.7 Tg), Brazil (18.5 Tg), and Indonesia (10.7 Tg) have the highest anthropogenic emissions, representing 14%, 9%, 7%, 5%, and 3% of the global total anthropogenic source, respectively. Uncertainty estimates come from an inversion ensemble with varied inversion parameters. Results provide an estimate of emissions from all countries in a globally consistent inverse modeling framework, serve as a direct comparison and aid of countries’ UNFCCC reporting, and provide up-to-date observational constraints on emissions from countries where reporting is unfeasible or out of date.

Anthropogenic methane emissions play an important role in exacerbating climate change, and thus there is a need for accurate and timely monitoring and mitigation of these emissions. With daily global coverage, TROPOMI, onboard Sentinel-5P, maps methane concentrations at 5.5 x 7 km2 resolution and can detect methane super-emitters (>~8 t hr-1) globally [1,2]. Here, we show how we detect, attribute, and quantify methane emissions from super-emitters using TROPOMI in combination with information from high-resolution satellite instruments to support the UNEP IMEO Methane Alert Response System (MARS). To determine optimal targets for high-resolution hyperspectral observations (e.g. PRISMA, EnMAP), we combine longer term TROPOMI data over persistent emitters. When emissions are transient, we combine TROPOMI with data from non-targeted high-resolution band imagers (also known as multispectral sensors) such as Sentinel-2 and Sentinel-3 to trace emissions to facility-level emission sources, in particular oil and gas infrastructure [3]. We illustrate how the combination of satellites with different overpass times and different spatial resolutions gives a comprehensive picture of these emissions. To evaluate the detections, we compare methane enhancements retrieved from band imagers with values from TROPOMI. Even when overpass times do not match, we achieve this by using transient emissions that result in methane plumes with a constant total mass, once detached from the source. Finally, we show how combining information from multiple satellites enables critical evaluation of the winds taken from global reanalysis products that underlie almost all high-resolution emission quantifications based on mass-balance methods. References[1]        Maasakkers JD, Varon DJ, Elfarsdóttir A, McKeever J, Jervis D, Mahapatra G, et al. Using satellites to uncover large methane emissions from landfills. Sci Adv 2022;8:eabn9683. https://doi.org/10.1126/sciadv.abn9683.[2]       Irakulis-Loitxate I, Guanter L, Maasakkers JD, Zavala-Araiza D, Aben I. Satellites Detect Abatable Super-Emissions in One of the World’s Largest Methane Hotspot Regions. Environ Sci Technol 2022;56:2143–52. https://doi.org/10.1021/acs.est.1c04873.[3]       Pandey, Sudhanshu, et al. "Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p." Remote Sensing of Environment 296 (2023): 113716. https://doi.org/10.1016/j.rse.2023.1137

Current methods for detecting atmospheric plumes and inferring point source rates from high-resolution satellite imagery are labor intensive and not scalable to the growing satellite dataset available for methane point sources. Here we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a machine learning U-Net architecture for image segmentation. The second step quantifies point source rate from the masked plume using wind speed information and either a convolution neural network (CNN) or a physics-based Integrated Mass Enhancement (IME) method. The algorithm can process 62 128×128 images per second on a single core. We train the algorithm with large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point source observability Ops = Q/(UWΔB) as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h -1 over surfaces with low background noise and succeeds for larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates while the CNN method is biased toward the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2-4 m s -1 is optimal for detection and quantification of point sources from satellite data.

Abstract. ​​​​​​​The ability to detect and quantify methane emissions from offshore platforms is of considerable interest in providing actionable feedback to industrial operators. While satellites offer a distinctive advantage for remote sensing of offshore platforms which may otherwise be difficult to reach, offshore measurements of methane from satellite instruments in the shortwave infrared are challenging due to the low levels of diffuse sunlight reflected from water surfaces. Here, we use the GHGSat satellite constellation in a sun glint configuration to detect and quantify methane emissions from offshore targets around the world. We present a variety of examples of offshore methane plumes, including the largest single emission at (84 000 ± 24 000) kg h−1 observed by GHGSat from the Nord Stream 2 pipeline leak in 2022 and the smallest offshore emission measured from space at (180 ± 130) kg h−1 in the Gulf of Mexico. In addition, we provide an overview of the constellation's offshore measurement capabilities. We measure a median column precision of 2.1 % of the background methane column density and estimate a detection limit, from analytical modelling and orbital simulations, that varies between 160 and 600 kg h−1 depending on the latitude and season.

<strong class="journal-contentHeaderColor">Abstract.</strong> Current methods for detecting atmospheric plumes and inferring point source rates from high-resolution satellite imagery are labor intensive and not scalable to the growing satellite dataset available for methane point sources. Here we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a machine learning U-Net architecture for image segmentation. The second step quantifies point source rate from the masked plume using wind speed information and either a convolution neural network (CNN) or a physics-based Integrated Mass Enhancement (IME) method. The algorithm can process 62 128&times;128 images per second on a single core. We train the algorithm with large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point source observability <em>O_ps</em> = <em>Q</em>/(<em>UW</em>&Delta;<em>B</em>) as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate <em>Q</em>, wind speed <em>U</em>, instrument pixel size <em>W</em>, and instrument-dependent background noise &Delta;<em>B</em>. We show that <em>O_ps</em> can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h^-1 over surfaces with low background noise and succeeds for larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates while the CNN method is biased toward the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2&ndash;4 m s^-1 is optimal for detection and quantification of point sources from satellite data.

The ability to detect and quantify methane emissions from offshore platforms is of considerable interest to provide actionable feedback to industrial operators. While satellites offer a distinctive advantage for remote sensing of offshore platforms which may otherwise be difficult to reach, offshore measurements of methane from satellite instruments in the shortwave-infrared are challenging due to the low levels of diffuse sunlight reflected from water surfaces. Here, we use the GHGSat satellite constellation in a sun glint configuration to detect and quantify methane emissions from offshore targets around the world. We present a variety of examples of offshore methane plumes, including the largest single emission at (84, 000 ± 24, 000) kg h −1 observed by GHGSat from the Nord Stream 2 pipeline leak in 2022 and the smallest offshore emission measured from space at (180 ± 130) kg h −1 in the Gulf of Mexico. In addition, we provide an overview of the constellation's offshore measurement capabilities. We measure a median column precision of 2.1% of the background methane column density and estimate a detection limit, from analytical modelling and orbital simulations, that varies between 160 kg h −1 and 600 kg h −1 depending on the latitude and season.

Abstract. A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit, providing daily global coverage of methane mixing ratios at a resolution of up to 7×5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a convolutional neural network to detect plume-like structures in the methane data and subsequently apply a support vector classifier to distinguish the emission plumes from retrieval artifacts. The models are trained on pre-2021 data and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021, with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA, and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, and for the latter, we find up to 10 facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification, and monitoring of these methane super-emitters, which is essential for mitigating their emissions.

<strong class="journal-contentHeaderColor">Abstract.</strong> We quantify weekly methane emissions at 0.25&deg;&times;0.3125&deg; (&asymp;25&times;25 km²) resolution from the Permian Basin, the largest oil production basin in the United States, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (&plusmn; standard deviation of weekly estimates) was 3.7 &plusmn; 0.9 Tg a^-1, higher than previous TROPOMI inversion estimates that may have used too-low prior emissions or biased background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and local natural gas spot price were significant drivers of variability in emissions over our study period, but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % &plusmn; 1.3 % and steadily decreased over the period from 5&ndash;6 % in 2018 to 3&ndash;4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above recent industry targets of &lt;0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts.

<strong class="journal-contentHeaderColor">Abstract.</strong> We quantify weekly methane emissions at 0.25&deg;&times;0.3125&deg; (&asymp;25&times;25 km²) resolution from the Permian Basin, the largest oil production basin in the United States, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (&plusmn; standard deviation of weekly estimates) was 3.7 &plusmn; 0.9 Tg a^-1, higher than previous TROPOMI inversion estimates that may have used too-low prior emissions or biased background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and local natural gas spot price were significant drivers of variability in emissions over our study period, but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % &plusmn; 1.3 % and steadily decreased over the period from 5&ndash;6 % in 2018 to 3&ndash;4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above recent industry targets of &lt;0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts.