We present results from a Very Large Methane Release (VLMR) experiment evaluating methane retrievals from the Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imagers (ABIs) and multiple low-Earth-orbit imagers with high point-source detection limits. The experiment coordinated observations of a U.S. gas pipeline blowdown with nine satellites, two aircraft, and a truck-based mobile laboratory. We used the GOES-16, -18, and -19 ABIs with revisits every 10 min to 7 s to quantify release magnitude and uncertainty. Best methane retrieval precision (7–8%) was achieved in the 7-s and 30-s mesoscale scan modes averaged to 5 min. Source-rate and mass estimates are broadly consistent across measurement platforms. Detectable emissions totaled 370±30 t over 44–65 min from two release points, ~25% lower than bottom-up expectations based on pipeline volume and nominal pressure, likely due to late-stage emissions below satellite detection limits. Our work provides a framework for evaluating high-detection-limit methane point-source imagers.

We present HyperGas, an open-source Python package for the retrieval and estimation of atmospheric greenhouse gas concentration enhancements and plume emission rates using data from hyperspectral imagers such as the PRecursore IperSpettrale della Missione Applicativa (PRISMA), the Environmental Mapping and Analysis Program (EnMAP), and the Earth Surface Mineral Dust Source Investigation (EMIT). The software is designed for compatibility with any three-dimensional hyperspectral radiance dataset. HyperGas supports multiple retrieval algorithms, including matched filter and lognormal matched filter, and offers two emission rate estimation methods: the integrated mass enhancement and cross-sectional flux approaches. The software provides a scalable batch-processing framework that supports data workflows from radiances to emission rates and an interactive graphical user interface that enables visualization of gas plumes. Built on high-level data structures such as xarray and CSV, HyperGas simplifies metadata handling and facilitates robust analysis and visualization. The package provides a robust foundation for community use and expansion. This toolkit aims to advance atmospheric monitoring capabilities and support both research and operational applications of greenhouse gas monitoring.

Continuous and global detection of large methane emissions is a crucial step for global warming mitigation. Satellite observations, such as from S5P/TROPOMI, combined with plume detection algorithms, can play a key role in this effort. However, not all TROPOMI plume detections that look like methane emission plumes are the result of actual emissions. A significant part of the plume-like features in the data are retrieval artifacts. Such artifacts could be the result of variations in elevation or albedo gradients, high concentrations of aerosols, coastal lines, water bodies, etc. Previous work approached the problem of plume-artifact classification by means of a Support Vector Machine Classifier (SVC), trained on an extensive set of observation-based scalar features designed by domain experts. However, such an approach limits the information scope received by the algorithm to what is deemed to be important by the experts, breaks the spatial relationship between pixels, and loses information during the process of statistical aggregation. In this study, we compare feature-based (SVC, Random Forest, XGBoost) and image-based (ResNet-18, ResNet-34) models for methane plume-artifact classification under balanced and imbalanced evaluation settings. To interpret the results, we apply SHAP-based explainability to both model families. Our findings provide practical guidance for model selection in operational methane-screening workflows such as the CAMS Methane Hotspot Explorer.

Understanding and independently validating carbon emissions from concentrated point sources is vital to support climate policy. Satellite-based quantifications of CO2 point source emissions have been limited by the spatial coverage of current satellite instruments. We combine three different satellite instruments to determine carbon monoxide (CO) and carbon dioxide (CO2) emissions of seven large cities and six industrial complexes. We first estimate CO emission rates using TROPOMI CO observations with the Cross-Sectional Flux method. Subsequently, CO2 emission rates are calculated by multiplying with the ratio of TROPOMI-observed CO enhancements and CO2 enhancements from OCO-2 and OCO-3, also representing the combustion efficiency. We use synthetic observations to validate our approach and show that the inclusion of TROPOMI CO observations increases the number of possible CO2 emission quantifications. Using 2018-2023 observations, we find lower CO emission rates for Delhi and Lahore than the EDGAR emission inventory. In contrast, our CO emission estimates exceed bottom-up inventory estimates for most industrial sources. This is caused by observed combustion efficiencies that are generally lower than those reported in emission inventories. Our CO2 emission estimates show better agreement with EDGAR than the CO emissions, especially for industrial sources. We find higher CO2 emission rates than EDGAR for the cities of Delhi, Lahore, and Cairo that better agree with the ODIAC inventory. Our work shows the importance of CO as a co-emitted species, and paves the way for a similar approach to be applied to the combination of TROPOMI, its successor Sentinel-5, and the future CO2M satellites.

Atmospheric methane concentrations increased at a rate of 0.7% year-1 over 2019-2024, but the causes are unclear. We conducted an analytical inversion of bias-corrected TROPOspheric Monitoring Instrument (TROPOMI) satellite observations to quantify 2019-2024 annual mean methane emissions and hemispheric OH concentrations. We find that the methane rise from 2019 to 2024 reflects an approach to steady state between 2019 sources and sinks (59% of the rise), augmented by increasing emissions (25%) and decreasing OH concentrations (16%). Global emissions increased from 571 teragrams (Tg) per year in 2019 to 601 Tg per year in 2021 and back to 575 Tg per year in 2024. 2019-2024 emission decreases from oil/gas and rice were offset by increases from livestock and waste. East Africa and South America were most responsible for the 2019-2024 increase in emissions. The weaker methane rise over 2022-2024 was driven mostly by increasing OH concentrations rather than decreasing emissions.

Methane, the second most important anthropogenic greenhouse gas, has a global warming potential more than 80 times that of carbon dioxide over a 20-year period. Given its decadal atmospheric lifetime, reducing anthropogenic methane emissions is critical for limiting near-term warming. The TROPOspheric Monitoring Instrument (TROPOMI) provides daily global methane satellite observations, enabling rapid detection of super-emitters. Here, we develop ML-SPERE, a machine-learning framework based on a convolutional neural network trained on simulated TROPOMI methane observations and meteorological data to estimate emission rates for super-emitters. ML-SPERE outperforms the Integrated Mass Enhancement (IME) method on simulated plumes that incorporate real TROPOMI backgrounds and missing spatial data, reducing the median absolute percentage error from 42.4 % to 24.3 % for well-observed methane plumes. At low wind speeds, IME estimates exhibit a negative bias and ML-SPERE estimates do not. Applied to TROPOMI observations of a 200-d well blowout in Kazakhstan, ML-SPERE typically shows agreement with TROPOMI inverse modeling results and TROPOMI IME estimates. Compared to TROPOMI IME estimates, ML-SPERE estimates show improved agreement with IME estimates derived from high-resolution point-source imagers. Global spatial patterns of methane emissions inferred from ML-SPERE and the IME method for all super-emitters found by TROPOMI in 2021 are broadly consistent, with notable regional differences in northern Russia (where transient pipeline emissions may not be well characterized by either method), the Congo Basin (where area source emissions may not be well characterized by either method), and southeastern Australia (where IME estimates are potentially negatively biased owing to predominantly low wind speeds). Mean estimated emission rates for this dataset aggregated by estimated source sector remain similar between both methods. Overall, improved performance on simulated plumes and consistency with independent estimates for real-world observations demonstrate the utility of ML-SPERE for quantifying TROPOMI methane super-emitters.

Robust estimates of methane emissions are critical for understanding their impacts on atmospheric warming and air quality, and for assessing methane mitigation strategies. Gridded inventories, such as the U.S. Environmental Protection Agency's Greenhouse Gas Inventory (EPA GHGI), the Emissions Database for Global Atmospheric Research (EDGAR 2024), and the National Oceanic and Atmospheric Administration's Fossil Fuel Oil and Gas inventory (NOAA FOG), are constructed to evaluate large-scale emission patterns and support identifying emission mitigation priorities and prioritizing future measurements. However, substantial differences across inventories complicate such assessments. We benchmark EPA GHGI, EDGAR 2024, and NOAA FOG against flux estimates from an atmospheric inversion of Greenhouse Gases Observing Satellite (GOSAT) data from 2012 to 2020 over the Contiguous United States (CONUS). A key technical challenge is the heterogeneous sensitivity of satellite-derived fluxes, which depends on measurement uncertainty, coverage, and inversion model configuration. We account for this heterogeneity by applying an inversion operator to each inventory prior to comparison with the GOSAT-based estimates. The GOSAT estimates are most sensitive to oil and gas and livestock emissions; oil and gas emissions are consistent with NOAA FOG (14.1 TgCH4yr1 in 2015), but exceed EPA GHGI and EDGAR, particularly across Texas, Oklahoma, and Louisiana. GOSAT-based livestock emissions exceed EPA GHGI and EDGAR by 1-2 TgCH4yr1, with the largest differences in the Midwest and California. Despite these discrepancies, both activity and satellite based estimates show no observable trends from 2012 to 2020 in fossil and livestock emissions.

Most satellite images have systematically missing pixels (i.e., missing data not at random (MNAR)) due to factors such as clouds. If not addressed, these missing pixels can lead to representation bias in automated feature extraction models. In this work, we show that spurious association between the label and the number of missing values in methane plume detection can cause the model to associate the coverage (i.e., the percentage of valid pixels in an image) with the label, subsequently under-detecting plumes in low-coverage images. We evaluate multiple imputation approaches to remove the dependence between the coverage and a label. Additionally, we propose a weighted resampling scheme during training that removes the association between the label and the coverage by enforcing class balance in each coverage bin. Our results show that both resampling and imputation can significantly reduce the representation bias without hurting balanced accuracy, precision, or recall. Finally, we evaluate the capability of the debiased models using these techniques in an operational scenario and demonstrate that the debiased models have a higher chance of detecting plumes in low-coverage images.

Cities around the world have united to form coalitions, like the C40 network, in pursuit of ambitious climate goals. These efforts often include reducing methane emissions. However, sources and magnitudes of urban methane emissions are not well known, and there is not currently a method to evaluate implementation of mitigation measures. Here, we fill this observational gap with a tracer-tracer approach using space-based observations of methane and carbon monoxide from the TROPOspheric Monitoring Instrument satellite instrument. We measure methane emissions of 92 global cities, including their broader metropolitan area, and find aggregate emissions of 31.2 Tg CH4/y (95%CI: 22.3, 40.4 Tg CH4/y) in 2023, equivalent to [Formula: see text]10% of the global anthropogenic methane budget. We track emissions for 72 of these cities (51 C40 cities and 21 non-C40 cities) from 2019-2023. Methane emissions from these cities weakly declined in 2020 followed by steady growth, with a 2.3 Tg aggregate increase over 4 y. This growth contributes minimally to the recent atmospheric methane surge. While C40 cities have largely pledged 34% reductions by 2030, we observe significant growth from 2020 to 2023 (10%, 95%CI: 2%, 17%), similar to growth observed in non-C40 cities (12%, 95%CI: [Formula: see text]1.5%, 25%). Inventories fail to capture observed growth, suggesting urban emissions are not well characterized, and mitigation approaches may not be optimally designed. For the C40 network to achieve its methane target (34% by 2030), steep, rapid reductions will be needed. Emission reductions of this magnitude would be detectable with the space-based approach used in this work.

Reducing global methane emissions is vital in combating climate change. Satellite-based instruments provide a way to independently monitor methane emissions from various sources at different scales, helping to assess the progress toward emission reduction targets. In this paper, we apply several data-driven methods to estimate methane emissions from the Secunda CTL (coal-to-liquids) synthetic fuel plant in South Africa, utilizing satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S5P) satellite and the GHGSat fleet of high-resolution commercial satellites. We find annual mean emissions of about 13-22 t/h based on S5P/TROPOMI observations. These results are consistent with estimates from an automated TROPOMI methane plume detection and quantification method. Estimates based on GHGSat observations from individual sources within the plant sum to about 6 t/h, on average. For comparison, Sasol, the operator of the Secunda CTL facility, reported methane emissions of 11.5 t/h for the period July 2023-June 2024, a value that falls between the TROPOMI- and GHGSat-based estimates. Our results highlight the value of satellite observations as a useful audit complementing reported emissions and demonstrate the importance of combining coarse- and fine-resolution data to monitor methane emissions at the plant and intrafacility level in complex sources.

The mitigation of methane emissions is one of the prime targets of global climate policy due to methane’s large contribution to global warming. Satellite instruments have proven to be very effective in mapping and tracking methane super-emitters. There are several new Copernicus Contributing Missions (CCMs) that are able to provide high resolution (~ 25m) methane abundance data that enables detection of emissions from individual facilities. We introduce a new Copernicus Atmosphere Monitoring Service (CAMS) service where we will use these CCMs to pinpoint individual methane sources all around the world to provide insights on emissions in support of mitigation efforts. The service will start with GHGSat data and aims to incorporate GEISAT and GESat data later. We will obtain observations over hundreds of methane hot spots and sites of interest around the world in support of climate policy. We process the satellite data starting from the methane abundance data provided by the CCMs using the SRON-developed HyperGas package. The data are standardized and potential methane plumes in the abundance field are automatically masked. Multiple expert operators then determine and agree on which masked features are true methane plumes. The automatically generated masks are then used for emission rate estimation using the integrated mass enhancement (IME) method. This methodology is calibrated using instrument-specific synthetic observations and will be evaluated using observations of controlled releases. Our semi-supervised approach allows for a consistent quantification of plumes over the full range of observations. Our processing is done independently from the analysis done by the CCMs themselves and thus serves as an evaluation. We also compare our results with bottom-up emission estimates such as included in the “TNO Emission Atlas”. This way, our work can provide a crucial link between satellite methane observations and facility level bottom-up inventories. We present our approach in consistently handling this large volume of data as well as initial results and interesting cases.

Magmatic intrusions into sedimentary rocks can mobilise carbon-based greenhouse gases, through the interaction between magma and organic-rich sediments. These processes have been linked to rapid climate changes in the past. However, we lack appropriate modern analogues for these systems, as most volcanic systems emit very little methane. In this study, we present the first confirmed satellite detection of methane emissions associated with a volcanic system, resulting from interactions between magma and sedimentary rocks. In early 2025, Fentale Volcano, Ethiopia, released >38.2 ± 3.9 kilo tonnes of methane, with 90% emitted within one month. Peak methane emission rates reached 157 ± 41 tonnes per hour, comparable to major industrial blowouts and orders of magnitude higher than typical volcanic systems emission rates, making this, to date, the largest observed natural point-source methane emission. The emissions followed the intrusion of ~1 km3 of magma into a 50 km long dyke that did not erupt. The release of methane and carbon dioxide coincided with localised ground subsidence, thermal anomalies and a persistent low-lying plume within Fentale’s caldera. We infer that the intrusion disrupted an impermeable cap, allowing for the sudden mobilisation of previously trapped gases. This single outburst, while small relative to annual emissions from natural and anthropogenic sources, demonstrates that some volcanoes can release methane episodically. These observations highlight the importance of satellite monitoring for detecting transient volcanic degassing and provide new insights into the mechanisms by which magmatic intrusions release carbon-based greenhouse gases.

The years 2023 and 2024 were characterized by unprecedented warming across the globe, underscoring the urgency of climate action. Robust science advice for decision makers on subjects as complex as climate change requires deep cross- and interdisciplinary understanding. However, navigating the ever-expanding and diverse peer-reviewed literature on climate change is enormously challenging for individual researchers. We elicited expert input through an online questionnaire (188 respondents from 45 countries) and prioritized 10 key advances in climate-change research with high policy relevance. The insights span a wide range of areas, from changes in methane and aerosol emissions to the factors shaping citizens' acceptance of climate policies. This synthesis and communications effort forms the basis for a science-policy report distributed to party delegations ahead of the 29th session of the Conference of the Parties (COP29) to inform their positions and arguments on critical issues, including heat-adaptation planning, comprehensive mitigation strategies, and strengthened governance in energy-transition minerals value chains.

The hydroxyl radical (OH) is the main oxidant in the troposphere and controls the lifetime of many atmospheric pollutants, including methane. Global annual-mean tropospheric OH concentrations ([OH‾]) have been inferred since the late 1970s using the methyl chloroform (MCF) proxy. However, concentrations of MCF are now approaching the detection limit, and a replacement proxy is urgently needed. Previous inversions of GOSAT (Greenhouse Gases Observing Satellite) satellite measurements of methane in the shortwave infrared (SWIR) have shown success in quantifying [OH‾] independently of methane emissions, and observing system simulations have suggested that satellite measurements in the thermal infrared (TIR) may provide additional constraints on OH. Here we combine SWIR and TIR satellite observations from the GOSAT and AIRS instruments, respectively, in a 3-year (2013–2015) analytical Bayesian inversion optimizing both methane emissions and OH concentrations. We examine how much information can be obtained about the interannual, seasonal, and latitudinal features of the OH distribution. We use information from MCF data and the ACCMIP ensemble of global atmospheric chemistry models to construct a full prior error covariance matrix for OH concentrations for use in the inversion. This is essential to avoid an overfitting of the observations. Our results show that GOSAT alone is sufficient to quantify [OH‾] and its interannual variability independently of methane emissions and that AIRS adds little information. The ability to constrain the latitudinal variability of OH is limited by strong error correlations. There is no information on OH at midlatitudes, but there is some information on the NH/SH interhemispheric ratio, showing this ratio to be lower than currently simulated in models. There is also some information on the seasonal variation in OH concentrations, although it mainly confirms the variation simulated by the models.

Abstract. Satellite observations from the Sentinel-5P TROPOMI instrument, combined with inverse modeling, provide a valuable resource for quantifying regional methane (CH4) emissions. This study compares the emissions estimated from variational inversions in 2019 over Europe (0.5° resolution) assimilating three TROPOMI products of dry-column methane mole fractions (XCH4). The SRON (v2.4, operational product), BLENDED (v1.0), and WFMD (v1.8) products are retrieved from distinct algorithms. They differ in coverage, error characterization, and XCH4 spatial distribution. Results indicate that the largest contributions to XCH4 differences may be attributed to aerosol scattering and sensitivity to albedo. The derived 2019 European CH4 emission budgets show a relative increase of +2 % for SRON, and a decrease of -1 %, -33 % and -9 %, respectively, for BLENDED, WFMD and surface-based inversions. Seasonal emissions are highly correlated across the inversions. Spatial emission patterns and optimized boundary conditions are similar for the non-independent SRON and BLENDED but differ substantially from WFMD. Evaluation with independent surface stations shows error reduction for about half of the sites, with BLENDED performing best. However, no product is systematically closer to the emissions estimated when assimilating surface observations. Observing System Simulation Experiments (OSSEs) are used to disentangle the drivers of differences between the posterior emissions. They reveal that observation density and errors, but also averaging kernels and prior profiles play a key role in the inversion's capacity to constrain the emissions. Using consistent error definition and quality filters increases the consistency of the OSSEs, paving the way for more consistent emission estimates.

Methane is a potent but short-lived greenhouse gas and rapid reductions of its anthropogenic emissions could help decrease near-term warming 1 . Solid waste emits methane through the decay of organic material, which amounts to about 10% of total anthropogenic methane emissions 2 . Satellite instruments 3 enable monitoring of strong methane hotspots 4 , including many strongly emitting urban areas that include solid waste disposal sites as most prominent sources 5 . Here we present a survey of methane emissions from 151 individual waste disposal sites across six continents using high-resolution satellite observations that can detect localized methane emissions above 100 kg h –1 . Within this dataset, we find that our satellite-based estimates generally show no correlation with reported or modelled emission estimates at facility scale. This reveals major uncertainties in the current understanding of methane emissions from waste disposal sites, warranting further investigations to reconcile bottom-up and top-down approaches. We also observe that managed landfills show lower emission per area than dumping sites, and that detected emission sources often align with the open non-covered parts of the facility where waste is added. Our results highlight the potential of high-resolution satellite observations to detect and monitor methane emissions from the waste sector globally, providing actionable insights to help improve emission estimates and focus mitigation efforts.

We quantify weekly methane emissions and trends from oil and gas production in the US Permian Basin for 2019-2023, and in nearby basins for 2022-2023, by analytical inversion of Tropospheric Monitoring Instrument (TROPOMI) satellite observations with the Integrated Methane Inversion (IMI) at 25 km resolution. Permian oil and gas emissions averaged 4.0 ± 1.1 Tg a-1 over 2019-2023, with large seasonal variation but little interannual variability. Methane intensity fell from 5.2 to 3.2% as production surged. Intensity in the New Mexico Permian fell from 4.5 to 2.1%, approaching the state's 2026 target of <2%. Emissions were on average 50 ± 10% higher in winter than summer, which we corroborate with Permian Basin Tower Network measurements, Insight M aircraft data, and GHGSat satellite observations. This seasonality may be driven in part by higher winter emissions from liquid storage tanks due to decreased separator efficiency in cold conditions. Similar but weaker seasonality along with decreasing emissions and intensities is found in weekly inversions for the Anadarko, Barnett, Eagle Ford, and Haynesville basins in 2022-2023. Our work suggests that better weatherization of oil and gas facilities could significantly reduce methane emissions.

We use satellite observations of atmospheric methane from the TROPOMI instrument to estimate total annual methane emissions for 2019–2023 from four large Southeast US landfills with gas collection and control systems. The emissions are on average 6× higher than the values reported by the landfills to the US Greenhouse Gas Reporting Program (GHGRP) which are used by the US Environmental Protection Agency for its national Greenhouse Gas Inventory (GHGI). We find increasing emissions over the 2019–2023 period whereas the GHGRP reports a decrease. The GHGRP requires gas-collecting landfills to estimate their annual emissions either with a recovery-first model (estimating emissions as a function of methane recovered) or a generation-first model (estimating emissions from a first-order decay applied to waste-in-place). All four landfills choose to use the recovery-first model, which yields emissions that are one-quarter of those from the generation-first model and decreasing over 2019–2023, in contrast with the TROPOMI observations. Our TROPOMI estimates for two of the landfills agree with the generation-first model, with increasing emissions over 2019–2023 due to increasing waste-in-place or decreasing methane recovery, and are still higher than the generation-first model for the other two landfills. Further examination of the GHGRP emissions from all reporting landfills in the US shows that the 19% decrease in landfill emissions reported by the GHGI over 2005–2022 reflects an increasing preference for the recovery-first model by the reporting landfills, rather than an actual emission decrease. The generation-first model would imply an increase in landfill emissions over 2013–2022, and this is more consistent with atmospheric observations.

UNEP's International Methane Emissions Observatory (IMEO) launched the Methane Alert and Response System (MARS) in 2023 to provide open, reliable, and actionable data to those individuals with the agency to act on them and ultimately reduce methane emissions. MARS uses satellite observations to detect and monitor large methane emissions and then notifies governments and companies worldwide. With the development of MARS, IMEO opened a new level of transparency that reveals dozens of large methane emissions around the world every week. Thanks to the synergistic use of more than a dozen different open-access satellite missions, combined with the development of Machine Learning models that support and optimize the work of the MARS analysis group, IMEO provides the largest open-access database of point source methane emissions detected with different satellites. At the same time, since its launch in January 2023, MARS has directly notified stakeholders of more than 1900 methane plumes linked to the oil and gas (O&G) sector in about 30 countries. As a result of these notifications, IMEO has confirmed a number of mitigated emission sources following stakeholder action. Throughout the MARS process, we also learn new information and lessons about the accuracy of our measurements, the root causes behind the observed emissions, and the real feasibility of mitigating emission sources under different scenarios and geographic areas, among others.While MARS notifications are currently on sent for recent O&G point source emissions, it also has the capability to detect and monitor emissions from other sectors, such as coal and waste. Additionally, we have the ability to explore satellite archive data to conduct more in-depth analyses of the historical behaviour of the emitters. As a result, IMEO is currently expanding MARS’ capacity to further support IMEO's scientific studies and its efforts towards increasing transparency in the metallurgical coal and waste sectors to drive emissions reduction.In this contribution, we will show case studies we have recently dealt with, lessons learned, improvements, and new data and methodologies integrated into MARS based on scientific research. We will also give an overview of IMEO’s efforts in the metallurgical coal sector and in the waste sector through scientific studies and with the support of remote sensing data generated through MARS.

Solid waste is the third largest source of anthropogenic methane, and mitigating emissions is crucial for addressing climate change. We combine three high-resolution (30-60 m) hyperspectral satellite imagers (EMIT, EnMAP, and PRISMA) to quantify emissions from 38 strongly emitting disposal sites across worldwide urban methane hotspots. The imagers give consistent emission estimates, with EMIT and EnMAP having better sensitivity than PRISMA. Total observed emissions add up to 230 ± 15 t h-1, representing 5% of reported global solid waste emissions. Our estimates exceed the facility-level Climate TRACE inventory by a factor of 1.8, while we only detect emissions from 9 of the inventory's 20 highest-emitting sites, highlighting the importance of facility-level information. Furthermore, multimonth observations reveal emission patterns potentially linked to facility operations. We estimate that these instruments could detect 35-60% of global landfill emissions, critically expanding on satellite instruments designed for methane and supporting emission mitigation.