91 citations as recorded by crossref.

1. 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane J. Maasakkers et al. 10.5194/acp-21-4339-2021
2. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. 10.3390/rs14020371
3. High-resolution assessment of coal mining methane emissions by satellite in Shanxi, China S. Peng et al. 10.1016/j.isci.2023.108375
4. Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. 10.3390/rs17030543
5. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
6. Estimating emissions of methane consistent with atmospheric measurements of methane and δ13C of methane S. Basu et al. 10.5194/acp-22-15351-2022
7. Towards a whole‐system framework for wildfire monitoring using Earth observations M. Crowley et al. 10.1111/gcb.16567
8. A new algorithm to generate a priori trace gas profiles for the GGG2020 retrieval algorithm J. Laughner et al. 10.5194/amt-16-1121-2023
9. Estimation of CH4emission based on an advanced 4D-LETKF assimilation system J. Bisht et al. 10.5194/gmd-16-1823-2023
10. Ensemble estimates of global wetland methane emissions over 2000–2020 Z. Zhang et al. 10.5194/bg-22-305-2025
11. Satellite-Based Monitoring of Methane Emissions from China’s Rice Hub R. Liang et al. 10.1021/acs.est.4c09822
12. Atmospheric methane isotopes identify inventory knowledge gaps in the Surat Basin, Australia, coal seam gas and agricultural regions B. Kelly et al. 10.5194/acp-22-15527-2022
13. Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions S. Zhu et al. 10.3390/rs17132321
14. A 130‐year global inventory of methane emissions from livestock: Trends, patterns, and drivers L. Zhang et al. 10.1111/gcb.16280
15. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations Z. Chen et al. 10.5194/acp-22-10809-2022
16. 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
17. 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
18. Decreasing methane emissions from China’s coal mining with rebounded coal production J. Gao et al. 10.1088/1748-9326/ac38d8
19. Locating and quantifying CH4 sources within a wastewater treatment plant based on mobile measurements J. Yang et al. 10.5194/acp-25-4571-2025
20. Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. 10.1038/s41598-022-19462-4
21. Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Z. Qu et al. 10.5194/acp-21-14159-2021
22. Order of magnitude wall time improvement of variational methane inversions by physical parallelization: a demonstration using TM5-4DVAR S. Pandey et al. 10.5194/gmd-15-4555-2022
23. Observed changes in China’s methane emissions linked to policy drivers Y. Zhang et al. 10.1073/pnas.2202742119
24. Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
25. 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
26. Identifying the main drivers of the spatiotemporal variations in wetland methane emissions during 2001–2020 Y. Hu et al. 10.3389/fenvs.2023.1275742
27. 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
28. 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
29. Evaluation of the Stratospheric Contribution to the Inter‐Annual Variabilities of Tropospheric Methane Growth Rates P. Zhang et al. 10.1029/2023GL103350
30. 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
31. Global Atmospheric δ13CH4 and CH4 Trends for 2000–2020 from the Atmospheric Transport Model TM5 Using CH4 from Carbon Tracker Europe–CH4 Inversions V. Mannisenaho et al. 10.3390/atmos14071121
32. Opinion: Beyond global means – novel space-based approaches to indirectly constrain the concentrations of and trends and variations in the tropospheric hydroxyl radical (OH) B. Duncan et al. 10.5194/acp-24-13001-2024
33. 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
34. 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
35. Accelerating methane growth rate from 2010 to 2017: leading contributions from the tropics and East Asia Y. Yin et al. 10.5194/acp-21-12631-2021
36. 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
37. Plant mediated methane efflux from a boreal peatland complex A. Korrensalo et al. 10.1007/s11104-021-05180-9
38. Bottom‐Up Evaluation of the Methane Budget in Asia and Its Subregions A. Ito et al. 10.1029/2023GB007723
39. African rice cultivation linked to rising methane Z. Chen et al. 10.1038/s41558-023-01907-x
40. 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
41. An integrated analysis of contemporary methane emissions and concentration trends over China using in situ and satellite observations and model simulations H. Tan et al. 10.5194/acp-22-1229-2022
42. 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
43. Monthly methane emissions in Chinese mainland provinces from 2013–2022 D. Cui et al. 10.1038/s41597-025-05107-4
44. 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
45. Evaluation of the High Altitude Lidar Observatory (HALO) methane retrievals during the summer 2019 ACT-America campaign R. Barton-Grimley et al. 10.5194/amt-15-4623-2022
46. Toward cleaner air and better health: Current state, challenges, and priorities W. Huang et al. 10.1126/science.adp7832
47. An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al. 10.3390/rs17050843
48. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps G. Hurtt et al. 10.1088/1748-9326/ac7407
49. Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
50. Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
51. Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. 10.1109/LGRS.2024.3379119
52. Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations X. Lu et al. 10.5194/acp-21-4637-2021
53. Historical trend of China's CH4 concentrations and emissions during 2003–2020 based on satellite observations, and their implications D. Chen et al. 10.1016/j.apr.2022.101615
54. 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
55. Evaluation of methane emission from MSW landfills in China, India, and the U.S. from space using a two-tier approach S. Zhang et al. 10.1016/j.jenvman.2025.124705
56. Satellite-Based Seasonal Fingerprinting of Methane Emissions from Canadian Dairy Farms Using Sentinel-5P P. Prajesh et al. 10.3390/cli13070135
57. Nutrient management may reduce global warming potential of rice cultivation in subtropical India K. Abbhishek et al. 10.1016/j.crsust.2022.100169
58. Verifying Methane Inventories and Trends With Atmospheric Methane Data J. Worden et al. 10.1029/2023AV000871
59. 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
60. 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
61. City-scale methane emissions from the midstream oil and gas industry: A satellite survey of the Zhoushan archipelago X. Yang et al. 10.1016/j.jclepro.2024.141673
62. What can we learn about tropospheric OH from satellite observations of methane? E. Penn et al. 10.5194/acp-25-2947-2025
63. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. 10.3390/rs14020375
64. Numerical analysis of CH4 concentration distributions over East Asia with a regional chemical transport model L. Qin et al. 10.1016/j.atmosenv.2023.120207
65. A gridded inventory of Canada’s anthropogenic methane emissions T. Scarpelli et al. 10.1088/1748-9326/ac40b1
66. Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution S. Zhu et al. 10.1007/s00376-022-1434-6
67. A XCO Retrieval Algorithm Coupled Spatial Correlation for the Aerosol and Carbon Detection Lidar Z. Pei et al. 10.1016/j.atmosenv.2023.119933
68. Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements C. Karacan et al. 10.1016/j.scitotenv.2025.179268
69. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes D. Cusworth et al. 10.1038/s43247-021-00312-6
70. Integrating Satellite Observations and Hydrological Models to Unravel Large TROPOMI Methane Emissions in South Sudan Wetlands Y. Albuhaisi et al. 10.3390/rs16244744
71. Quantifying methane emission baselines with high-resolution satellite data to support China’s emission control H. Zhong et al. 10.1016/j.scib.2025.04.047
72. A review of organophosphonates, their natural and anthropogenic sources, environmental fate and impact on microbial greenhouse gases emissions – Identifying knowledge gaps A. Furtak et al. 10.1016/j.jenvman.2024.120453
73. 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
74. Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions S. Ma et al. 10.1029/2021AV000408
75. 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
76. Anthropogenic emission is the main contributor to the rise of atmospheric methane during 1993–2017 Z. Zhang et al. 10.1093/nsr/nwab200
77. Rapid rise in premature mortality due to anthropogenic air pollution in fast-growing tropical cities from 2005 to 2018 K. Vohra et al. 10.1126/sciadv.abm4435
78. Recent Slowdown of Anthropogenic Methane Emissions in China Driven by Stabilized Coal Production G. Liu et al. 10.1021/acs.estlett.1c00463
79. 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
80. 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
81. Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations R. Janardanan et al. 10.1088/1748-9326/ad2436
82. Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. 10.1021/acs.est.4c07415
83. Trends in atmospheric methane concentrations since 1990 were driven and modified by anthropogenic emissions R. Skeie et al. 10.1038/s43247-023-00969-1
84. 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
85. Application of nitrogen-rich sunflower husks biochar promotes methane oxidation and increases abundance of Methylobacter in nitrogen-poor soil A. Kubaczyński et al. 10.1016/j.jenvman.2023.119324
86. Trends and variability in methane concentrations over the Southeastern Arabian Peninsula D. Francis et al. 10.3389/fenvs.2023.1177877
87. 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
88. Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins L. Shen et al. 10.5194/acp-22-11203-2022
89. Jump in Tropospheric Methane Concentrations in 2020–2021 and Slowdown in 2022–2024: New Hypotheses on Causation T. Ming et al. 10.3390/atmos16040406
90. The Role of Emission Sources and Atmospheric Sink in the Seasonal Cycle of CH4 and δ13-CH4: Analysis Based on the Atmospheric Chemistry Transport Model TM5 V. Kangasaho et al. 10.3390/atmos13060888
91. Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area J. Sheng et al. 10.1088/1748-9326/ac24d1