Using new satellite observations and atmospheric inverse modeling, we report methane emissions from the Permian Basin, which is among the world’s most prolific oil-producing regions and accounts for >30% of total U.S. oil production. Based on satellite measurements from May 2018 to March 2019, Permian methane emissions from oil and natural gas production are estimated to be 2.7 ± 0.5 Tg a⁻¹, representing the largest methane flux ever reported from a U.S. oil/gas-producing region and are more than two times higher than bottom-up inventory-based estimates. This magnitude of emissions is 3.7% of the gross gas extracted in the Permian, i.e., ~60% higher than the national average leakage rate. The high methane leakage rate is likely contributed by extensive venting and flaring, resulting from insufficient infrastructure to process and transport natural gas. This work demonstrates a high-resolution satellite data–based atmospheric inversion framework, providing a robust top-down analytical tool for quantifying and evaluating subregional methane emissions.

Rapid identification of anomalous methane sources in oil/gas fields could enable corrective action to fight climate change. The GHGSat‐D satellite instrument measuring atmospheric methane with 50‐meter spatial resolution was launched in 2016 to demonstrate space‐based monitoring of methane point sources. Here we report the GHGSat‐D discovery of an anomalously large, persistent methane source (10‐43 tons h^‐1, detected in over 50% of observations) at a gas compressor station in Central Asia, together with additional sources (4‐32 tons h^‐1) nearby. The TROPOMI satellite instrument confirms the magnitude of these large emissions going back to at least November 2017. We estimate that these sources released 142 ± 34 kilotons of methane to the atmosphere from February 2018 through January 2019, comparable to the four‐month total emission from the well‐documented Aliso Canyon blowout.

As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Here, we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfills. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this approach, we detect and analyze strongly emitting landfills (3 to 29 t hour ⁻¹ ) in Buenos Aires, Delhi, Lahore, and Mumbai. Using TROPOMI data in an inversion, we find that city-level emissions are 1.4 to 2.6 times larger than reported in commonly used emission inventories and that the landfills contribute 6 to 50% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane superemitters at the facility level.

Methane emissions due to accidents in the oil and natural gas sector are very challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio’s reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions.

Incidents involving loss of control of oil/gas wells can result in large but variable emissions whose impact on the global methane budget is currently unknown. On November 1, 2019, a gas well blowout was reported in the Eagle Ford Shale. By combining satellite observations at different spatial and temporal scales, we quantified emissions 10 times during the 20‐day event. Our multisatellite synthesis captures both the short‐term dynamics and total integrated emissions of the blowout. Such detailed event characterization was previously not possible from space and difficult to do with surface measurements. We present 30‐m methane and carbon dioxide plumes from the PRISMA satellite, which let us estimate flare combustion efficiency (87%). Integrating emissions across all satellites, we estimate 4,800 ± 980 metric tons lost methane. Blowouts occur across the globe and multisatellite observations can help to determine their pervasiveness, enable corrective action, and quantify their contribution to global methane budgets.

Wetland methane (CH₄) emissions comprise about one‐third of the global CH₄ source. The latitudinal distribution and climate sensitivity of wetland CH₄ fluxes are the key determinants of the global CH₄‐climate feedback. However, large differences exist between bottom‐up estimates, informed by ground‐based flux measurements, and top‐down estimates derived from spaceborne total column CH₄. Despite the extensive coverage of satellite CH₄ concentration observations, challenges remain with using top‐down estimates to test bottom‐up models, mainly because of the uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, and the confounding role of the posterior error covariance structures. Here, we use satellite‐based top‐down CH₄ flux estimates (2010–2012) to test and refine 42 bottom‐up estimates of wetland emissions that use a range of hypothesized wetland extents and process controls. Our comparison between bottom‐up models and satellite‐based fluxes innovatively accounts for cross‐correlations and spatial uncertainties typically found in top‐down inverse estimates, such that only the information from satellite observations and the atmospheric transport model is kept as a constraint. We present a satellite‐constrained wetland CH₄ ensemble product derived from assembling the highest‐performance bottom‐up models, which estimates global wetland CH₄ emissions of 148 (117–189, 5th–95th percentile) Tg CH₄ yr⁻¹. We find that tropical wetland emissions contribute 72% (63%–85%) to the global wetland total. We also find that a lower‐than‐expected temperature sensitivity agrees better with atmospheric CH₄ measurements. Overall, our approach demonstrates the potential for using satellites to quantitatively refine bottom‐up wetland CH₄ emission estimates, their latitudinal distributions, and their sensitivity to climate.