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Anthropogenic emissions of methane (CH4) have made a considerable contribution towards the Earth's changing radiative budget since pre-industrial times. This is because large amounts of methane are emitted from human activities, and the global warming potential of methane is high. The majority of anthropogenic fossil methane emissions to the atmosphere originate from a large number of small (point) sources. Thus, detection and accurate, rapid quantification of such emissions are vital to enable the reduction of emissions to help mitigate future climate change. There exist a number of instruments on satellites that measure radiation at methane-absorbing wavelengths, which have sufficiently high spatial resolution that can be used for detecting plumes of highly spatially localised methane “point sources” (areas on the order of m2 to km2). Searching for methane plumes in methane-sensitive satellite images using classical methods, such as thresholding and clustering, can be useful but is time-consuming and often involves empirical decisions. Here, we develop a deep neural network to identify and quantify methane point source emissions from hyperspectral imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite with 30 m spatial resolution. The moderately high spectral and spatial resolution, as well as considerable global coverage and free access to data, makes PRISMA a good candidate for methane plume detection. The neural network was trained with simulated synthetic methane plumes generated with the large eddy simulation extension of the Weather Research and Forecasting model (WRF-LES), which we embedded into PRISMA images. The deep neural network was successful at locating plumes with a F1 score, precision, and recall of 0.95, 0.96, and 0.92, respectively, and was able to quantify emission rates with a mean error of 24 %. The neural network was furthermore able to locate several plumes in real-world images. We have thus demonstrated that our method can be effective in locating and quantifying methane point source emissions in near-real time from 30 m resolution satellite data, which can aid us in mitigating future climate change.

In recent years, Digital Twins of the Environment are emerging as useful tools for decision support to help address specific, complex environmental problems. We understand a Digital Twin as a digital representation of a physical system that is data-driven, has some predictive capabilities, and can provide decision support for stakeholders. With the increase in computer power, the development of advanced machine-learning techniques, and the ever-increasing wealth of Earth Observation data available, Digital Twins have a lot of potential to help solve or manage important environmental challenges, such as climate change. Here we present our recent work undertaken as part of our ESA EO4Society project IMITATE (‘Introducing Machine learning Into Targeted Analysis of Terrestrial Ecosystems’), where we developed the foundations of a Digital Twin of the carbon cycle over Europe using a model-data fusion. Our approach is based on the development of machine-learning emulators of the land surface model JULES (Joint UK Land Environment Simulator), which is the terrestrial component of the UK Earth System Model. For this project, we have focused on a single parameter/process within JULES (Gross Primary Productivity, GPP) and a limited geographical domain (Europe). These emulators have several advantages. They are really fast and much less computationally intensive, which means we can run simulations in milliseconds. They can also be used without expert knowledge of model or HPC facilities, embedded within web-applications, etc. In addition, we can take advantage of explainable AI methods to better understand the relationship between the inputs and the outputs. Finally, by driving the emulators (which have been trained on JULES data and thus learned its physical basis) with Earth Observation data, we produce a new GPP dataset, which is based on the physics from JULES and constrained by observations. Our emulators match JULES remarkably well, with an average R2 better than 0.98. In addition, using these emulators with input EO data such as Land Surface Temperature and FAPAR, the emulated GPP is comparable to other products (MODIS and Sen4GPP), and has good agreement with observations from some FLUXNET stations. Thus, we present here a novel approach with very promising results, and there are still many possibilities for improvements, further developments and new applications, which we will be exploring in future.

The use of satellite NO2 data for air quality studies is increasingly revealing the need for observations with higher spatial and temporal resolution. The study of the NO2 diurnal cycle, global sub-urban-scale observations, and identification of emission point sources are some examples of important applications not possible at the resolution provided by current instruments. One way to achieve increased spatial resolution is to reduce the spectral information needed for the retrieval, allowing both dimensions of conventional 2-D detectors to be used to record spatial information. In this work we investigate the use of 10 discrete wavelengths with the well-established differential optical absorption spectroscopy (DOAS) technique for NO2 slant column density (SCD) retrievals. To test the concept we use a selection of individual OMI and TROPOMI Level 1B swaths from various regions around the world, which contain a mixture of clean and heavily polluted areas. To discretise the data we simulate a set of Gaussian optical filters centred at various key wavelengths of the NO2 absorption cross section. We perform SCD retrievals of the discrete data using a simple implementation of the DOAS algorithm and compare the results with the corresponding Level 2 SCD products, namely QA4ECV for OMI and the operational TROPOMI product. For OMI the overall results from our discrete-wavelength retrieval are in very good agreement with the Level 2 data (mean difference

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.Methane satellite observations are now possible from a number of instruments with very high spatial resolution which allow to map methane emission plumes from individual emission sources. In this work, we explore the capabilities of three satellites with different specifications, spatial coverage and spatial resolutions ranging from metres (WorldView-3; multi-spectral) to tens of metres (PRISMA; hyperspectral) to kilometres (TROPOMI; hyperspectral). This leads to different capabilities for detecting and quantifying methane point sources that can complement each other. Thanks to its good coverage, TROPOMI Level 2 XCH4 data (from IUP Bremen) allows to locate areas with methane anomalies which can then be further analysed with targeted PRISMA and WorldView-3 (WV-3) observations to quantify methane emissions from small point sources.In our work, we use a fast data-driven retrieval algorithm to derive methane column enhancements from PRISMA and Worldview-3, combined with a statistical method to identify methane plumes and the well-established Integrated Mass Enhancement (IME) method to derive emission flux rates. We developed a simulation framework to characterise and test our approach. This makes use of synthetic methane plumes generated with the Large Eddy Simulation extension of the Weather Research and Forecasting model (WRF-LES) that have been embedded into WV-3 or PRISMA images. To further advance the plume detection methods and to allow automatisation, we have developed a deep learning model for WV-3 or PRISMA based on the WRF-LES simulations.In this presentation, we will describe and characterise our plume detection method for three satellite systems covering a wide range of spatial resolutions and we will introduce our deep learning approach. Both methods have been applied to case studies with a focus on emissions from coal mining in South Africa and Australia which we will use to discuss and contrast the different methods and satellite systems.

Methane (CH4) is a greenhouse gas emitted from natural emissions that pre-dominantly come from wetland sources, and from a wide range of anthropogenic sources including livestock, oil–gas systems, landfills, coalmines, wastewater management, and rice cultivation. Since the global warming potential of CH4 far outweighs that of carbon dioxide (CO2), this means that policies aimed at reducing CH4 emissions are key to combating climate change on shorter timescales. Significant gains can be achieved by avoiding accidental or uncontrolled CH4 emissions from industrial or waste-treatment sites and methods for active monitoring of such sites will play an important role to support this.For many localised emitters such as landfill sites, it is often difficult to ascertain the level of compliance and effectiveness of waste management protocols used by local authorities, particularly in emerging and developing countries. Some landfill sites are so poorly regulated that there is little handle on the scale and intensity of CH4 emissions and pollution originating from these sites. Furthermore, in uncontrolled landfill sites, waste can spontaneously combust and lead to the emission of flammable CH4 gas from decomposition of biological material further aggravating pollution in densely populated cities. For example, in the case of Indian megacities such as Delhi, some landfill sites exceeded their full capacity well over a decade ago and authorities are making important efforts to implement alternative measures to manage and reduce the waste in these landfill sites.Satellite sensors can map CH4 emission plumes from strong point sources that can be undetected by sparse ground-based networks and they provide us with a powerful new tool to characterize and quantify the rate and intensity of landfill CH4 emissions. The recently launched satellite missions such as the Sentinel 5p (carrying onboard the TROPOMI spectrometer) offers the potential to observe such CH4 plumes on a global scale but with relatively coarse spatial resolution (7km). This is complemented by high-resolution sensors such as the GHGSat imager that offer much improved pixel sizes (tens of m) that can map CH4 sources at a much finer scale but with very limited coverage.Here we present an evaluation of landfill CH4 emission rates for landfill sites located across Indian megacities using a combination of TROPOMI and space-borne imager observations. We will show an analysis of CH4 observations over India using the University of Bremen TROPOMI/WFMD CH4 product to identify CH4 enhancements across Indian landfill sites. We focus on the Ghazipur landfill site in the megacity of Delhi as well as sites in Mumbai and West Bengal and use the cross-sectional flux method to determine the largescale CH4 emissions originating from these sites. We will discuss the challenges in estimating CH4 source rates from point sources and present the approach used to detect and quantify CH4 from Indian landfills from TROPOMI. We will present an evaluation of our estimates against in-country Indian municipal solid waste emission inventories as verification and demonstrate the value of satellite observations in supporting authorities implement corrective actions to better manage landfill emissions.

The benefits of urban green and blue infrastructure (UGI) are widely discussed, but rarely take into account local conditions or contexts. Although assessments increasingly consider the demand for the ecosystem services that UGI provides, they tend to only map the spatial pattern of pressures such as heat, or air pollution, and lack a wider understanding of where the beneficiaries are located and who will benefit most. We assess UGI in five cities from four continents with contrasting climate, socio-political context, and size. For three example services (air pollution removal, heat mitigation, accessible greenspace), we run an assessment that takes into account spatial patterns in the socio-economic demand for ecosystem services and develops metrics that reflect local context, drawing on the principles of vulnerability assessment. Despite similar overall levels of UGI (from 35 to 50% of urban footprint), the amount of service provided differs substantially between cities. Aggregate cooling ranged from 0.44 °C (Leicester) to 0.98 °C (Medellin), while pollution removal ranged from 488 kg PM2.5/yr (Zomba) to 48,400 kg PM2.5/yr (Dhaka). Percentage population with access to nearby greenspace ranged from 82% (Dhaka) to 100% (Zomba). The spatial patterns of pressure, of ecosystem service, and of maximum benefit within a city do not necessarily match, and this has implications for planning optimum locations for UGI in cities.

The wellbeing of disadvantaged children in low-income countries (LIC) is often compromised by a range of environmental risk factors, as well as by limited access to supports and services. The aim of this exploratory study was to understand service provision barriers for children's wellbeing in an urban informal settlement in Dhaka, Bangladesh, by complementing stakeholder perspectives with a spatial method and a knowledge exchange framework. The research literature and official records informed interviews with 19 stakeholders, who identified interdisciplinary services. Visual geospatial analysis mapped services in relation to child wellbeing. These maps informed a knowledge exchange participatory workshop with 21 stakeholders, who co-produced recommendations related to child-centeredness and safety, community involvement, and accessible resources. Such a participatory approach combining stakeholder perspectives with geospatial analysis can be a useful tool in low-resource contexts.

The use of satellite NO2 data for air quality studies is increasingly revealing the need for observations with higher spatial and temporal resolution. The study of the NO2 diurnal cycle, global sub-urban scale observations, and identification of emission point sources are some examples of important applications not possible at the resolution provided by current instruments. One way to achieve increased spatial resolution is to reduce the spectral information needed for the retrieval, allowing both dimensions of conventional 2-D detectors to be used to record spatial information. In this work we investigate the use of ten discrete wavelengths with the well-established Differential Optical Absorption Spectroscopy (DOAS) technique for NO2 slant column density (SCD) retrievals. To test the concept we use a selection of individual OMI and TROPOMI Level 1B swaths from various regions around the world which contain a mixture of clean and heavily polluted areas. To discretise the data we simulate a set of Gaussian optical filters centred at various key wavelengths of the NO2 absorption cross section. We perform SCD retrievals of the discrete data using a simple implementation of the DOAS algorithm and compare the results with the corresponding Level 2 SCD products, namely QA4ECV for OMI and the operational TROPOMI product. For OMI the overall results from our discrete-wavelength retrieval are in very good agreement with the Level 2 data (mean difference