Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG) in terms of its overall effect on climate radiative forcing. The atmospheric residence time of methane is considerably shorter than that of carbon dioxide, but its warming potential significantly stronger. Methane is produced from natural sources such as wetlands, and as a result of human activities, such as the oil and gas industry. A small number of anomalously large anthropogenic point sources are a major contribution to the total global anthropogenic methane emission budget, thus early detection of such sources has great potential for climate mitigation.

Satellite observations allow the detection and quantification of methane sources even in remote areas where monitoring would otherwise be difficult or costly. Methane satellite observations are now possible from a wide variety of instruments with very high spatial resolution. In this work, we explore the capabilities of three satellites with different specifications and spatial resolutions ranging from metres (WorldView-3; multi-spectral) to tens of metres (PRISMA; hyperspectral) to kilometres (TROPOMI; hyperspectral), to detect and quantify methane point sources.

We use TROPOMI Level 2 XCH4 data from IUP Bremen and statistical methods to find areas with methane anomalies in selected regions of the world. We then use PRISMA and WorldView-3 (WV-3) observations to zoom in those areas, and identify and quantify methane emissions from small point sources.

For PRISMA and WV-3 we use a fast data-driven retrieval algorithm to derive the methane column enhancements. We then use an automated system based on a deep learning model to detect and isolate methane plumes. This model has been trained using synthetic methane plumes generated with the Large Eddy Simulation extension of the Weather Research and Forecasting model (WRF-LES), and embedded in the WV-3 or PRISMA radiances. Finally, we calculate the emission flux rates using the well-known Integrated Mass Enhancement (IME) method.

We present several case studies of observations of methane plumes from the three satellites, and characterise their performance. This work has been done by researchers from the National Centre for Earth Observation based at the University of Leicester, University of Leeds and University of Edinburgh as part of a project funded by the UK Natural Environment Research Council (NERC).

The importance of reducing methane emissions to mitigate climate change in the short term has been recognised at the highest political level. At COP26 over 100 countries signed up to the Methane Pledge to reduce methane emissions by 30% by 2030. The International Methane Emission Observatory (IMEO) was recently launched by the UN to support companies and governments in reducing their methane emissions by providing actionable data based on a.o. methane measurements. In addition, the European Commission has prominently identified the role of satellites to support its goal to reduce methane emissions. As such TROPOMI -in combination with other satellites- can play an important role to support these efforts.

The Tropospheric Monitoring Instrument (TROPOMI) aboard ESA’s Sentinel-5P satellite was launched in 2017 and provides daily global coverage of methane concentrations at up to 7 x 5.5 km2 resolution. These data can be used to detect persistent methane emissions as well as large emission events such as accidents in the natural gas industry. As TROPOMI provides 10-20 million observations a day, we use a machine learning technique to detect the methane super-emitter signals. This machine learning approach allows constant global monitoring. While some plumes can be attributed to a specific source using just TROPOMI data, its resolution usually does not enable that. Therefore, we use the TROPOMI super-emitter detections to guide high-resolution instruments such as GHGSat, PRISMA, and Sentinel-2 to pinpoint the exact source(s) responsible, information needed for mitigation. For persistent sources, we use long-term TROPOMI data combined with metrological data to pinpoint the source’s location as precisely as possible. The meter-scale observations of high-resolution instruments over limited domains can then be used to identify the exact facilities responsible for the enhancements seen in TROPOMI and estimate their emissions. In this way we have been able to identify super-emitting landfills, oil/gas facilities, and coal mines. This information can be used to inform the operators allowing – for example – gas leaks to be fixed. The satellite-based emission estimates can also be used to evaluate reported emissions. We will show the latest results from this synergistic use of these different satellites.

Combining TROPOMI with the high spatial resolution satellites is a very powerful tool to not only understand but also mitigate anthropogenic methane emissions. As such we have already communicated the exact locations of quite a number of methane super emitters to the IMEO for further action.

The Paris Agreement foresees to establish a transparency framework that builds upon inventory-based national greenhouse gas emission reports, complemented by independent emission estimates derived from atmospheric measurements through inverse modelling. The capability of such a Monitoring and Verification Support (MVS) capacity to constrain fossil fuel emissions to a sufficient extent has not yet been assessed. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of an MVS capacity.

Here we present a CCFFDAS that operates at the resolution of the CO2M sensor, i.e. 2km by 2km, over a 200 km by 200 km region around Berlin. It combines models of sectorial fossil fuel CO2 emissions and biospheric fluxes with the Community Multiscale Air Quality model (coupled to a model of the plume rise from large power plants) as observation operator for XCO2 and tropospheric column NO2 measurements. Inflow from the domain boundaries is treated as extra unknown to be solved for by the CCFFDAS, which also includes prior information on the process model parameters. We discuss the sensitivities (Jacobian matrix) of simulated XCO2 and NO2 troposheric columns with respect to a) emissions from power plants, b) emissions from the surface and c) the lateral inflow and quantify the respective contributions to the observed signal. The Jacobian representation of the complete modelling chain allows us to evaluate data sets of simulated random and systematic CO2M errors in terms of posterior uncertainties in sectorial fossil fuel emissions. We provide assessments of XCO2 alone and in combination with NO2 on the posterior uncertainty in sectorial fossil fuel emissions for two 1-day study periods, one in winter and one in summer. We quantify the added value of the observations for emissions at a single point, at the 2km by 2km scale, at the scale of Berlin districts, and for Berlin and further cities in our domain. This means the assessments include temporal and spatial scales typically not covered by inventories. Further, we quantify the effect of better information of atmospheric aerosol, provided by a multi-angular polarimeter (MAP) onboard CO2M, on the posterior uncertainties.

The assessments differentiate the fossil fuel CO2 emissions into two sectors, an energy generation sector (power plants) and the complement, which we call “other sector”. We find that XCO2 measurements alone provide a powerful constraint on emissions from larger power plants and a constraint on emissions from the other sector that increases when aggregated to larger spatial scales. The MAP improves the impact of the CO2M measurements for all power plants and for the other sector on all spatial scales. Over our study domain, the impact of the MAP is particularly high in winter. NO2 measurements provide a powerful additional constraint on the emissions from power plants and from the other sector. We explore the effect of the random component in the error of the NO2 measurements on the posterior uncertainty of inferred fossil fuel emissions.

The United Nations Paris Agreement requires countries to reduce their greenhouse gas emissions. Transparent monitoring of CO2 emission reductions is desirable at multiple spatial scales, including the scale of individual facilities such as fossil fuel burning power plants. Here, we present a case study on monitoring CO2 emissions from Poland’s Bełchatów coal-burning power plant (the highest CO2 emitting power plant in Europe), using space-based observations from NASA’s Orbiting Carbon Observatory 2 and 3 (OCO-2 and OCO-3) missions. In addition to standard observations from these missions, we also use the ‘Target’ observing mode from OCO-2 and ‘Snapshot Area Mapping (SAM)’ mode of OCO-3. The emission estimates are compared with reported power generation, demonstrating the ability to detect emission changes over time at the facility scale. This research gives us a glimpse of the potential capabilities of future CO2 imaging missions like the Copernicus Anthropogenic CO2 Monitoring Mission (CO2M) and reinforces the value of future space-based CO2 data for verifying local-scale CO2 emission reductions with the potential to support the transparency framework under the Paris Agreement.

Accurate and up-to-date greenhouse gas (GHG) emission inventories are essential in developing targeted policies designed to reach net zero targets. Efficiently processing the large volumes of data being produced by satellites allows us to detect changes in anthropogenic emissions of GHGs. Satellite observations are an integral part of up to date emission reporting and monitoring. Directly relating GHG observations to emission hotspots is non-trivial due to the long life times of GHGs and the influence of natural components on the CO2 and methane fluxes. By analysing co-emitted species from combustion, we can determine the major emission sources of GHGs. We have developed a unique analysis tool, using a convolutional neural network (CNN), to identify plumes of nitrogen dioxide (NO2), a tracer of combustion, from NO2 column data collected by the TROPOspheric Monitoring Instrument (TROPOMI). Our approach allows us to exploit the growing volume of satellite data available to determine the locations of emission hotspots around the globe on a daily time scale. We train the deep learning model using six thousand 28 x 28-pixel images of TROPOMI data (corresponding to ~266 x 133 km2) to find emission plumes of various shapes and sizes. The model can identify plumes with a success rate of more than 90%. Over our study period (July 2018 to June 2020), we detect over 310,000 individual NO2 plumes, each with a corresponding location and timestamp. We can relate these locations to known emission hotspots such as cities, power stations and oil and gas production with over 9% of the detected plumes located over China. Ship tracks through the Suez Canal, South East Asia and around The Cape of Good Hope also become visible within the processed data. Our tool shows mixed results when compared to gas flaring regions, highlighting the difficulty of creating an accurate dataset for this type of emission. We have attempted to remove the influence of open biomass burning using correlative high-resolution thermal infrared data from the Visible Infrared Imaging Radiometer Suite (VIIRS). We also find persistent NO2 plumes from regions where inventories do not currently include emissions, including mid-Africa and Siberia, demonstrating the potential of this tool to help update inventories. Using an established anthropogenic CO2 emission inventory (ODIAC), we find that our NO2 plume distribution captures 92 % of total CO2 emissions, with the remaining 8 % mostly due to a large number of sources smaller than 0.2 gC/m2/day for which our NO2 plume model is less sensitive. We believe our type of analysis, used in conjunction with other earth observation products, can form part of a suit of products to improve estimates of anthropogenic emissions of greenhouse gases.

Space-based measurements of carbon dioxide (CO2) are the backbone of the global and national-scale carbon monitoring systems that are currently being developed to support and verify greenhouse gas emission reduction measures. Current and planned satellite missions such as JAXA’s GOSAT and NASA’s OCO series and the European Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission aim to constrain national and regional-scale emissions down to scales of urban agglomerations and large point sources with emissions in excess of ~10 MtCO2/year.

We report on the demonstrator mission “CO2Image”, now in Phase B, which is planned for launch in 2026. The mission will complement the suite of planned CO2 sensors by zooming in on facility-scale emissions, detecting and quantifying emissions from point sources as small as 1 MtCO2/year. A fleet of CO2Image sensors would be able to monitor nearly 90% of the CO2 emissions from coal-fired power plants worldwide. The key feature of the mission is a target region approach, covering tiles of ~50x50km2 extent with a resolution of 50x50m2. Thus, CO2Image will be able to resolve plumes from individual localized sources, essentially providing super-resolution nests for survey missions such as CO2M.

Here, we present the instrument concept which is based on a spaceborne push-broom imaging grating spectrometer measuring spectra of reflected solar radiation in the SWIR-2 spectral window around 2µm. It relies on a comparatively simple spectral setup with a single spectral window and a moderate spectral resolution of approximately ~ 1 nm. The instrument is designed to fly in a sun-synchronous orbit at an altitude of 500 to 600 km. We further discuss the overall mission goals and evaluate the mission concept in terms of e.g. optimal local overpass time and sampling strategy.