85 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. Spatiotemporal decoupling CH4 emission from economic growth and future trend in categorized Chinese cities C. Xu et al. 10.1016/j.eiar.2025.107859
3. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
4. Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al. 10.1038/s41598-024-58995-8
5. Global Methane Budget 2000–2020 M. Saunois et al. 10.5194/essd-17-1873-2025
6. 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
7. 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
8. Global observational coverage of onshore oil and gas methane sources with TROPOMI M. Gao et al. 10.1038/s41598-023-41914-8
9. Simulation and Error Analysis of Methane Detection Globally Using Spaceborne IPDA Lidar X. Zhang et al. 10.3390/rs15133239
10. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. 10.3390/rs14020371
11. 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
12. High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills H. Nesser et al. 10.5194/acp-24-5069-2024
13. 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 CH4 ObsPack) and satellite (GOSAT) atmospheric observations X. Lu et al. 10.5194/acp-22-395-2022
14. Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al. 10.3390/rs16142525
15. Tracking regional CH4 emissions through collocated air pollution measurement: a pilot application and robustness analysis in China Y. Li & B. Zheng 10.1038/s41612-025-01011-1
16. Derivation of Emissions From Satellite‐Observed Column Amounts and Its Application to TROPOMI NO2 and CO Observations K. Sun 10.1029/2022GL101102
17. Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al. 10.5194/acp-24-10441-2024
18. Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations L. Estrada et al. 10.5194/gmd-18-3311-2025
19. Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al. 10.1007/s12524-024-01925-y
20. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes D. Cusworth et al. 10.1038/s43247-021-00312-6
21. Natural Gas Leakage Ratio Determined from Flux Measurements of Methane in Urban Beijing Y. Huangfu et al. 10.1021/acs.estlett.4c00573
22. 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
23. 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
24. Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
25. Estimated regional CO2 flux and uncertainty based on an ensemble of atmospheric CO2 inversions N. Chandra et al. 10.5194/acp-22-9215-2022
26. 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
27. 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
28. Assessing the impact of natural factors on CH₄ concentrations in Chinese cities from 2009 to 2023 Z. Linjing et al. 10.1080/01431161.2025.2526004
29. Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al. 10.5194/amt-16-1597-2023
30. Retrieving the Vertical Profile of Greenhouse Gas Using Fourier Transform Spectrometer (FTS) - Part II: Methane (CH4) M. Kim et al. 10.5572/KOSAE.2024.40.3.361
31. 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
32. Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. 10.5194/acp-25-797-2025
33. Estimating ground-level CH4 concentrations inferred from Sentinel-5P J. Qin et al. 10.1080/01431161.2023.2240028
34. 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
35. Monitoring greenhouse gases (GHGs) in China: status and perspective Y. Sun et al. 10.5194/amt-15-4819-2022
36. 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
37. Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites Q. Ni et al. 10.3390/rs15184499
38. What can we learn about tropospheric OH from satellite observations of methane? E. Penn et al. 10.5194/acp-25-2947-2025
39. Integrating Satellite Observations and Hydrological Models to Unravel Large TROPOMI Methane Emissions in South Sudan Wetlands Y. Albuhaisi et al. 10.3390/rs16244744
40. Revising the coal mining CH4 emission factor based on multiple inventories and atmospheric inversion approach at one of the world's largest coal production areas: Shanxi province, China X. Shi et al. 10.1016/j.scitotenv.2025.178616
41. Local and regional enhancements of CH4, CO, and CO2 inferred from TCCON column measurements K. Mottungan et al. 10.5194/amt-17-5861-2024
42. Spatiotemporal variations in atmospheric CH4 concentrations and enhancements in northern China based on a comprehensive dataset: ground-based observations, TROPOMI data, inventory data, and inversions P. Han et al. 10.5194/acp-25-4965-2025
43. Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. 10.3390/rs17030543
44. Satellite-Based Monitoring of Methane Emissions from China’s Rice Hub R. Liang et al. 10.1021/acs.est.4c09822
45. 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
46. Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission C. Chan Miller et al. 10.5194/amt-17-5429-2024
47. Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al. 10.3390/atmos16030279
48. Cold‐Season Methane Fluxes Simulated by GCP‐CH4 Models A. Ito et al. 10.1029/2023GL103037
49. The Spatial and Temporal Distribution Patterns of XCH4 in China: New Observations from TROPOMI J. Zhang et al. 10.3390/atmos13020177
50. Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al. 10.5194/amt-16-89-2023
51. Quantifying Methane Emissions Using Satellite Data: Application of the Integrated Methane Inversion (IMI) Model to Assess Danish Emissions A. Vara-Vela et al. 10.3390/rs16234554
52. Emissions of climate-altering species from open vegetation fires in the Mediterranean region - A review on methods and data R. Hundal et al. 10.1016/j.scitotenv.2024.177713
53. 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
54. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. 10.3390/rs14020375
55. Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 10.1088/1748-9326/adad02
56. China's methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system X. Kou et al. 10.1016/j.apr.2024.102333
57. Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al. 10.3390/rs17040634
58. Monthly and annual oscillation of methane and ozone in eastern India using Sentinel-5P TROPOMI: assessment and validation A. Ghosh & S. Manna 10.1007/s11356-024-35755-9
59. Global Estimation of Surface Methane Using Low-Earth Orbit Satellite Data and Machine Learning: Comparison of Linear and Nonlinear Models D. Lee et al. 10.7780/kjrs.2025.41.2.1.12
60. Assessing methane emissions from collapsing Venezuelan oil production using TROPOMI B. Nathan et al. 10.5194/acp-24-6845-2024
61. 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
62. Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method Q. Tu et al. 10.5194/acp-24-4875-2024
63. Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. 10.1016/j.jenvman.2025.126309
64. 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
65. Verifying Methane Inventories and Trends With Atmospheric Methane Data J. Worden et al. 10.1029/2023AV000871
66. 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
67. 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
68. 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
69. 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
70. Monitoring dust retention variations in different functional zones based on leaf magnetism and the influence of green belt spatial layouts on leaf dust retention Z. Tao et al. 10.1007/s10661-025-13813-0
71. Declining short-term emission control opportunity for major events in Chinese cities H. Wang et al. 10.1038/s44284-025-00233-x
72. Evaluation of the methane full-physics retrieval applied to TROPOMI ocean sun glint measurements A. Lorente et al. 10.5194/amt-15-6585-2022
73. Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions S. Zhu et al. 10.3390/rs17132321
74. 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
75. 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
76. 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
77. 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
78. 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
79. Monthly methane emissions in Chinese mainland provinces from 2013–2022 D. Cui et al. 10.1038/s41597-025-05107-4
80. 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
81. 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
82. 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
83. Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge Z. Qu et al. 10.1073/pnas.2402730121
84. A Method to Estimate Clear-Sky Albedo of Paddy Rice Fields T. Sun et al. 10.3390/rs14205185
85. 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