49 citations as recorded by crossref.

1. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations Z. Chen et al. 10.5194/acp-22-10809-2022
2. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
3. Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al. 10.1038/s41598-024-58995-8
4. Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations D. Varon et al. 10.5194/gmd-15-5787-2022
5. Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater B. de Foy et al. 10.1088/1748-9326/acc118
6. Global observational coverage of onshore oil and gas methane sources with TROPOMI M. Gao et al. 10.1038/s41598-023-41914-8
7. Simulation and Error Analysis of Methane Detection Globally Using Spaceborne IPDA Lidar X. Zhang et al. 10.3390/rs15133239
8. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. 10.3390/rs14020371
9. Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States D. Cusworth et al. 10.1073/pnas.2202338119
10. Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) atmospheric observations X. Lu et al. 10.5194/acp-22-395-2022
11. Derivation of Emissions From Satellite‐Observed Column Amounts and Its Application to TROPOMI NO2 and CO Observations K. Sun 10.1029/2022GL101102
12. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes D. Cusworth et al. 10.1038/s43247-021-00312-6
13. Assessing the Relative Importance of Satellite-Detected Methane Superemitters in Quantifying Total Emissions for Oil and Gas Production Areas in Algeria S. Naus et al. 10.1021/acs.est.3c04746
14. Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion J. McNorton et al. 10.5194/acp-22-5961-2022
15. Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
16. Estimated regional CO2flux and uncertainty based on an ensemble of atmospheric CO2inversions N. Chandra et al. 10.5194/acp-22-9215-2022
17. 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. 10.3788/AOS231293
18. Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics X. Lu et al. 10.1073/pnas.2217900120
19. Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al. 10.5194/amt-16-1597-2023
20. Methane, carbon dioxide, hydrogen sulfide, and isotopic ratios of methane observations from the Permian Basin tower network V. Monteiro et al. 10.5194/essd-14-2401-2022
21. Estimating ground-level CH4 concentrations inferred from Sentinel-5P J. Qin et al. 10.1080/01431161.2023.2240028
22. Co-benefits for net carbon emissions and rice yields through improved management of organic nitrogen and water B. Liu et al. 10.1038/s43016-024-00940-z
23. Monitoring greenhouse gases (GHGs) in China: status and perspective Y. Sun et al. 10.5194/amt-15-4819-2022
24. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations L. Shen et al. 10.1038/s41467-023-40671-6
25. Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites Q. Ni et al. 10.3390/rs15184499
26. A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al. 10.5194/amt-16-3787-2023
27. Cold‐Season Methane Fluxes Simulated by GCP‐CH4 Models A. Ito et al. 10.1029/2023GL103037
28. The Spatial and Temporal Distribution Patterns of XCH4 in China: New Observations from TROPOMI J. Zhang et al. 10.3390/atmos13020177
29. Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al. 10.5194/amt-16-89-2023
30. Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action Z. Chen et al. 10.5194/acp-23-5945-2023
31. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. 10.3390/rs14020375
32. A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources X. Yu et al. 10.5194/acp-23-3325-2023
33. Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations T. Scarpelli et al. 10.5194/acp-22-3235-2022
34. Verifying Methane Inventories and Trends With Atmospheric Methane Data J. Worden et al. 10.1029/2023AV000871
35. Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. 10.3390/atmos14040758
36. The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates J. Worden et al. 10.5194/acp-22-6811-2022
37. CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes A. Tsuruta et al. 10.3390/rs15061620
38. East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis R. Liang et al. 10.5194/acp-23-8039-2023
39. Evaluation of the methane full-physics retrieval applied to TROPOMI ocean sun glint measurements A. Lorente et al. 10.5194/amt-15-6585-2022
40. Assessment of methane emissions from oil, gas and coal sectors across inventories and atmospheric inversions K. Tibrewal et al. 10.1038/s43247-023-01190-w
41. Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data A. Vara-Vela et al. 10.5194/gmd-16-6413-2023
42. Methane emission and influencing factors of China's oil and natural gas sector in 2020–2060: A source level analysis S. Sun et al. 10.1016/j.scitotenv.2023.167116
43. Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations D. Varon et al. 10.5194/acp-23-7503-2023
44. Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. 10.1016/j.rse.2024.114039
45. Using portable low-resolution spectrometers to evaluate Total Carbon Column Observing Network (TCCON) biases in North America N. Mostafavi Pak et al. 10.5194/amt-16-1239-2023
46. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. 10.5194/acp-22-9617-2022
47. CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model D. Pendergrass et al. 10.5194/gmd-16-4793-2023
48. A Method to Estimate Clear-Sky Albedo of Paddy Rice Fields T. Sun et al. 10.3390/rs14205185
49. Evaluation of temporal changes in methane content in the atmosphere for areas with a very high rice concentration based on Sentinel-5P data K. Kozicka et al. 10.1016/j.rsase.2023.100972