218 citations as recorded by crossref.

1. Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater B. de Foy et al. https://doi.org/10.1088/1748-9326/acc118
2. Spatial and temporal patterns of atmospheric column-averaged methane (XCH4) over tropical Thailand from Sentinel-5P TROPOMI satellite observations W. Kanchanachat et al. https://doi.org/10.1016/j.atmosenv.2026.122139
3. Development of a continuous UAV-mounted air sampler and application to the quantification of CO2 and CH4 emissions from a major coking plant T. Han et al. https://doi.org/10.5194/amt-17-677-2024
4. Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al. https://doi.org/10.1038/s41598-024-58995-8
5. Revised methane emissions factors and spatially distributed annual carbon fluxes for global livestock J. Wolf et al. https://doi.org/10.1186/s13021-017-0084-y
6. A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0) A. Bloom et al. https://doi.org/10.5194/gmd-10-2141-2017
7. Deep-Learning-Based Gas Leak Source Localization From Sparse Sensor Data D. Badawi et al. https://doi.org/10.1109/JSEN.2022.3202134
8. Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions Z. Deng et al. https://doi.org/10.5194/essd-14-1639-2022
9. Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations P. Sadavarte et al. https://doi.org/10.1021/acs.est.1c03976
10. Balancing aggregation and smoothing errors in inverse models A. Turner & D. Jacob https://doi.org/10.5194/acp-15-7039-2015
11. Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations R. Janardanan et al. https://doi.org/10.3390/rs12030375
12. Estimating 2010–2015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations S. Baray et al. https://doi.org/10.5194/acp-21-18101-2021
13. 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane J. Maasakkers et al. https://doi.org/10.5194/acp-21-4339-2021
14. Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications M. Reuter et al. https://doi.org/10.5194/amt-13-789-2020
15. Reduced U.S. methane emissions during the COVID-19 pandemic S. Ibarra-Espinosa et al. https://doi.org/10.1039/D6EA00004E
16. Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling S. Miller et al. https://doi.org/10.5194/bg-13-1329-2016
17. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. https://doi.org/10.3390/rs14020371
18. Minimum detection limits of the TROPOMI satellite sensor across North America and their implications for measuring oil and gas methane emissions L. Dubey et al. https://doi.org/10.1016/j.scitotenv.2023.162222
19. 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. https://doi.org/10.5194/acp-25-4965-2025
20. Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG A. Thorpe et al. https://doi.org/10.5194/amt-10-3833-2017
21. Detection of Methane Emission from a Local Source Using GOSAT Target Observations A. Kuze et al. https://doi.org/10.3390/rs12020267
22. Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories F. Wang et al. https://doi.org/10.3390/rs11212489
23. A high-resolution (0.1° × 0.1°) inventory of methane emissions from Canadian and Mexican oil and gas systems J. Sheng et al. https://doi.org/10.1016/j.atmosenv.2017.02.036
24. Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia J. Shaw et al. https://doi.org/10.1029/2021GB007261
25. Underestimates of methane from intensively raised animals could undermine goals of sustainable development M. Hayek & S. Miller https://doi.org/10.1088/1748-9326/ac02ef
26. Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3523022
27. Atmospheric Carbon and Transport – America (ACT‐America) Data Sets: Description, Management, and Delivery Y. Wei et al. https://doi.org/10.1029/2020EA001634
28. A large increase in U.S. methane emissions over the past decade inferred from satellite data and surface observations A. Turner et al. https://doi.org/10.1002/2016GL067987
29. Assimilation of satellite NO2 observations at high spatial resolution using OSSEs X. Liu et al. https://doi.org/10.5194/acp-17-7067-2017
30. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. https://doi.org/10.3390/atmos11030231
31. Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign 2019 M. Makarova et al. https://doi.org/10.5194/amt-14-1047-2021
32. A 15-year record of CO emissions constrained by MOPITT CO observations Z. Jiang et al. https://doi.org/10.5194/acp-17-4565-2017
33. Comparing mass balance and adjoint methods for inverse modeling of nitrogen dioxide columns for global nitrogen oxide emissions M. Cooper et al. https://doi.org/10.1002/2016JD025985
34. Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions S. Ma et al. https://doi.org/10.1029/2021AV000408
35. A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al. https://doi.org/10.5194/amt-16-3787-2023
36. How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observing system simulation experiments with the GEOS-Chem adjoint model (v35) X. Yu et al. https://doi.org/10.5194/gmd-14-7775-2021
37. Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010 S. Pandey et al. https://doi.org/10.5194/acp-16-5043-2016
38. A new aerial approach for quantifying and attributing methane emissions: implementation and validation J. Dooley et al. https://doi.org/10.5194/amt-17-5091-2024
39. Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? Z. Tan et al. https://doi.org/10.5194/acp-16-12649-2016
40. Influences of Uncertainties in the STT Flux on Modeled Tropospheric Methane Z. Wang https://doi.org/10.1029/2023JD039107
41. Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. https://doi.org/10.5194/acp-16-14371-2016
42. Contribution of oil and natural gas production to renewed increase in atmospheric methane (2007–2014): top–down estimate from ethane and methane column observations P. Hausmann et al. https://doi.org/10.5194/acp-16-3227-2016
43. Atmospheric CO and CH4 time series and seasonal variations on Reunion Island from ground-based in situ and FTIR (NDACC and TCCON) measurements M. Zhou et al. https://doi.org/10.5194/acp-18-13881-2018
44. Mineralization of organic matter in boreal lake sediments: rates, pathways, and nature of the fermenting substrates F. Clayer et al. https://doi.org/10.5194/bg-17-4571-2020
45. Constraints on methane emissions in North America from future geostationary remote-sensing measurements N. Bousserez et al. https://doi.org/10.5194/acp-16-6175-2016
46. Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone B. Duncan et al. https://doi.org/10.1029/2019RG000652
47. An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data M. Lunt et al. https://doi.org/10.5194/acp-19-14721-2019
48. Comparison of Methane Detection Using Shortwave and Longwave Infrared Hyperspectral Sensors Under Varying Environmental Conditions L. Zimmerman & J. Kerekes https://doi.org/10.1109/JSTARS.2023.3247246
49. Review of methane emission source tracing methods in oilfield regions Y. Liu et al. https://doi.org/10.1016/j.jgsce.2025.205708
50. High-resolution inversion of urban methane emissions in the Chengdu–Chongqing economic circle using ground-based observations and a dynamic error Bayesian framework Z. Xia et al. https://doi.org/10.1016/j.jclepro.2026.148229
51. 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. https://doi.org/10.5194/acp-24-5069-2024
52. Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US J. Sheng et al. https://doi.org/10.5194/amt-11-6379-2018
53. Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources R. Bun et al. https://doi.org/10.1007/s11027-018-9791-2
54. What are the greenhouse gas observing system requirements for reducing fundamental biogeochemical process uncertainty? Amazon wetland CH4 emissions as a case study A. Bloom et al. https://doi.org/10.5194/acp-16-15199-2016
55. Atmospheric methane dynamics and its associations with environmental factors in the mid-Indo-Gangetic region (Bihar), India A. Dass et al. https://doi.org/10.1016/j.jenvman.2025.128456
56. The challenge of reconciling bottom-up agricultural methane emissions inventories with top-down measurements R. Desjardins et al. https://doi.org/10.1016/j.agrformet.2017.09.003
57. Methyl, Ethyl, and Propyl Nitrates: Global Distribution and Impacts on Reactive Nitrogen in Remote Marine Environments J. Fisher et al. https://doi.org/10.1029/2018JD029046
58. Rain-fed pulses of methane from East Africa during 2018–2019 contributed to atmospheric growth rate M. Lunt et al. https://doi.org/10.1088/1748-9326/abd8fa
59. Detecting high-emitting methane sources in oil/gas fields using satellite observations D. Cusworth et al. https://doi.org/10.5194/acp-18-16885-2018
60. Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra H. Oshio et al. https://doi.org/10.3390/rs12193155
61. Using Multiscale Ethane/Methane Observations to Attribute Coal Mine Vent Emissions in the San Juan Basin From 2013 to 2021 A. Meyer et al. https://doi.org/10.1029/2022JD037092
62. Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al. https://doi.org/10.1021/acs.est.4c07415
63. Recent methane surges reveal heightened emissions from tropical inundated areas X. Lin et al. https://doi.org/10.1038/s41467-024-55266-y
64. Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model I. Stanevich et al. https://doi.org/10.5194/gmd-13-3839-2020
65. A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations S. Miller et al. https://doi.org/10.1002/2016GB005419
66. Evaluation of single-footprint AIRS CH4 profile retrieval uncertainties using aircraft profile measurements S. Kulawik et al. https://doi.org/10.5194/amt-14-335-2021
67. 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. https://doi.org/10.5194/acp-22-1229-2022
68. 2010–2016 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors J. Sheng et al. https://doi.org/10.5194/acp-18-12257-2018
69. On Methane Leaks from Pipelines in Bryan and College Station, Texas, USA K. Koch et al. https://doi.org/10.2174/1874282301610010056
70. The recent increase of atmospheric methane from 10 years of ground-based NDACC FTIR observations since 2005 W. Bader et al. https://doi.org/10.5194/acp-17-2255-2017
71. Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling R. Janardanan et al. https://doi.org/10.3390/rs9090941
72. The Carbon Mapper emissions monitoring system R. Duren et al. https://doi.org/10.5194/amt-18-6933-2025
73. Methane emissions are predominantly responsible for record-breaking atmospheric methane growth rates in 2020 and 2021 L. Feng et al. https://doi.org/10.5194/acp-23-4863-2023
74. Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales A. Turner et al. https://doi.org/10.5194/acp-18-8265-2018
75. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations L. Shen et al. https://doi.org/10.1038/s41467-023-40671-6
76. Spatial and temporal change patterns of near-surface CO2 and CH4 concentrations in different permafrost regions on the Mongolian Plateau from 2010 to 2017 S. Adiya et al. https://doi.org/10.1016/j.scitotenv.2021.149433
77. High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion R. Janardanan et al. https://doi.org/10.3390/rs17172932
78. Global CH4 fluxes derived from JAXA/GOSAT lower-tropospheric partial column data and the CarbonTracker Europe-CH4 atmospheric inverse model A. Tsuruta et al. https://doi.org/10.5194/acp-25-7829-2025
79. An Intercomparison of Underground Coal Mine Methane Emission Estimation in Shanxi, China: S5P/TROPOMI vs. GF-5B/AHSI Z. Yang et al. https://doi.org/10.3390/rs18040603
80. Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign S. Krautwurst et al. https://doi.org/10.5194/acp-21-17345-2021
81. Recovery of sparse urban greenhouse gas emissions B. Zanger et al. https://doi.org/10.5194/gmd-15-7533-2022
82. Assessing climate and air quality benefits of methane mitigation for U.S. landfills A. Folorunsho et al. https://doi.org/10.1016/j.jenvman.2026.128707
83. Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale S. Jongaramrungruang et al. https://doi.org/10.5194/amt-14-7999-2021
84. Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Z. Qu et al. https://doi.org/10.5194/acp-21-14159-2021
85. Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH4 data with weak-constraint four-dimensional variational data assimilation I. Stanevich et al. https://doi.org/10.5194/acp-21-9545-2021
86. A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources X. Yu et al. https://doi.org/10.5194/acp-23-3325-2023
87. A critical review of green growth indicators in G7 economies from 1990 to 2019 K. Herman et al. https://doi.org/10.1007/s11625-023-01397-y
88. Characterisation of methane variability and trends from near-infrared solar spectra over Hefei, China Y. Tian et al. https://doi.org/10.1016/j.atmosenv.2017.11.001
89. Retrieval of Metop-A/IASI N2O Profiles and Validation with NDACC FTIR Data B. Barret et al. https://doi.org/10.3390/atmos12020219
90. Conversion of surface CH4 concentrations from GOSAT satellite observations using XGBoost algorithm Y. Wan et al. https://doi.org/10.1016/j.atmosenv.2023.119694
91. Simultaneous shipborne measurements of CO2, CH4 and CO and their application to improving greenhouse-gas flux estimates in Australia B. Bukosa et al. https://doi.org/10.5194/acp-19-7055-2019
92. Methane emissions from dairies in the Los Angeles Basin C. Viatte et al. https://doi.org/10.5194/acp-17-7509-2017
93. MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery S. Jongaramrungruang et al. https://doi.org/10.1016/j.rse.2021.112809
94. A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico’s national inventory of greenhouse gases and compounds T. Scarpelli et al. https://doi.org/10.1088/1748-9326/abb42b
95. Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate L. Feng et al. https://doi.org/10.1038/s41467-022-28989-z
96. Investigation of spectral bands and sensor parameters for methane emission detection imaging spectrometer H. Varchand et al. https://doi.org/10.1016/j.jqsrt.2024.109100
97. Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. https://doi.org/10.1038/s41598-022-19462-4
98. Greenhouse gas column observations from a portable spectrometer in Uganda N. Humpage et al. https://doi.org/10.5194/amt-17-5679-2024
99. Rapid summer methane emission decline in high-latitude plains linked to 2021 drought M. Zhao et al. https://doi.org/10.1038/s43247-026-03433-y
100. Tropospheric emissions: Monitoring of pollution (TEMPO) P. Zoogman et al. https://doi.org/10.1016/j.jqsrt.2016.05.008
101. Revisiting global fossil fuel and biofuel emissions of ethane Z. Tzompa‐Sosa et al. https://doi.org/10.1002/2016JD025767
102. Methane emissions from underground gas storage in California A. Thorpe et al. https://doi.org/10.1088/1748-9326/ab751d
103. Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases E. Solazzo et al. https://doi.org/10.5194/acp-21-5655-2021
104. An Unexpected Seasonal Cycle in U.S. Oil and Gas Methane Emissions L. Hu et al. https://doi.org/10.1021/acs.est.4c14090
105. Interpreting contemporary trends in atmospheric methane A. Turner et al. https://doi.org/10.1073/pnas.1814297116
106. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane Y. Zhang et al. https://doi.org/10.5194/acp-18-15959-2018
107. Cattle, climate and complexity: food security, quality and sustainability of the Australian cattle industries I. Lean & P. Moate https://doi.org/10.1111/avj.13072
108. 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. https://doi.org/10.5194/gmd-15-5787-2022
109. A simple and quick sensitivity analysis method for methane isotopologues detection with GOSAT-TANSO-FTS E. Malina et al. https://doi.org/10.14324/111.444/ucloe.000013
110. Hydrocarbon Tracers Suggest Methane Emissions from Fossil Sources Occur Predominately Before Gas Processing and That Petroleum Plays Are a Significant Source A. Tribby et al. https://doi.org/10.1021/acs.est.2c00927
111. Rapid Methane Flux Estimation Combining MethaneSAT and Sentinel‐5P Observations: A Case Study of Turkmenistan Y. Huang et al. https://doi.org/10.1029/2025GL119369
112. Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. https://doi.org/10.1016/j.rse.2022.112966
113. Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions X. Yu et al. https://doi.org/10.5194/acp-21-951-2021
114. Large XCH4 anomaly in summer 2013 over northeast Asia observed by GOSAT M. Ishizawa et al. https://doi.org/10.5194/acp-16-9149-2016
115. Methane emissions from a waste treatment site: Numerical analysis of aircraft-based data Y. Cai et al. https://doi.org/10.1016/j.agrformet.2020.108102
116. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. https://doi.org/10.1088/1748-9326/ac8754
117. Assessment of the climate commitments and additional mitigation policies of the United States J. Greenblatt & M. Wei https://doi.org/10.1038/nclimate3125
118. High-resolution inversion of methane emissions in the Southeast US using SEAC4RS aircraft observations of atmospheric methane: anthropogenic and wetland sources J. Sheng et al. https://doi.org/10.5194/acp-18-6483-2018
119. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space A. Kuze et al. https://doi.org/10.5194/amt-9-2445-2016
120. Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter S. Heerah et al. https://doi.org/10.1029/2021JD034785
121. Democratizing climate change mitigation pathways using modernized stabilization wedges N. Johnson & I. Staffell https://doi.org/10.1126/science.adr2118
122. Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. https://doi.org/10.3390/atmos14040758
123. Atmospheric methane isotopes identify inventory knowledge gaps in the Surat Basin, Australia, coal seam gas and agricultural regions B. Kelly et al. https://doi.org/10.5194/acp-22-15527-2022
124. Evaluating urban methane emissions from space using TROPOMI methane and carbon monoxide observations G. Plant et al. https://doi.org/10.1016/j.rse.2021.112756
125. Uncertainty and retrieval sensitivity in TROPOMI-based methane inversions over the North Slope of Alaska R. Ward et al. https://doi.org/10.5194/amt-19-813-2026
126. Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations D. Varon et al. https://doi.org/10.1021/acs.est.0c01213
127. Assessing the Iterative Finite Difference Mass Balance and 4D‐Var Methods to Derive Ammonia Emissions Over North America Using Synthetic Observations C. Li et al. https://doi.org/10.1029/2018JD030183
128. 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. https://doi.org/10.3390/rs15061620
129. XCO2 over East Asia: Assessing consistency, growth, and seasonality from multiple satellites and models Y. Yang et al. https://doi.org/10.1016/j.atmosres.2026.108974
130. A Coupled CH4, CO and CO2 Simulation for Improved Chemical Source Modeling B. Bukosa et al. https://doi.org/10.3390/atmos14050764
131. Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. https://doi.org/10.3390/atmos15040449
132. Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution S. Zhu et al. https://doi.org/10.1007/s00376-022-1434-6
133. Artificial Intelligence Approach for Estimating Dairy Methane Emissions S. Jeong et al. https://doi.org/10.1021/acs.est.1c08802
134. Improvements of a low-cost CO2 commercial nondispersive near-infrared (NDIR) sensor for unmanned aerial vehicle (UAV) atmospheric mapping applications Y. Liu et al. https://doi.org/10.5194/amt-15-4431-2022
135. Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission C. Chan Miller et al. https://doi.org/10.5194/amt-17-5429-2024
136. Seasonal variability of stratospheric methane: implications for constraining tropospheric methane budgets using total column observations K. Saad et al. https://doi.org/10.5194/acp-16-14003-2016
137. Quantifying CO emissions from boreal wildfires by assimilating TROPOMI and TCCON observations S. Voshtani et al. https://doi.org/10.5194/acp-25-15527-2025
138. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. https://doi.org/10.3390/rs14020375
139. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps G. Hurtt et al. https://doi.org/10.1088/1748-9326/ac7407
140. California’s methane super-emitters R. Duren et al. https://doi.org/10.1038/s41586-019-1720-3
141. Emerging reporting and verification needs under the Paris Agreement: How can the research community effectively contribute? L. Perugini et al. https://doi.org/10.1016/j.envsci.2021.04.012
142. Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India T. Mathew et al. https://doi.org/10.5194/acp-26-4453-2026
143. Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH4 observations R. Parker et al. https://doi.org/10.1016/j.rse.2018.02.011
144. ASSESSING THE IMPACT OF CHANGES IN METHANE CONCENTRATION ON TROPOSPHERIC OZONE CONCENTRATIONS USING LONG⁻TERM FUTURE SCENARIO H. WATANABE et al. https://doi.org/10.2208/jscejj.25-27036
145. Estimating methane emissions in California's urban and rural regions using multitower observations S. Jeong et al. https://doi.org/10.1002/2016JD025404
146. A gridded inventory of Canada’s anthropogenic methane emissions T. Scarpelli et al. https://doi.org/10.1088/1748-9326/ac40b1
147. The CHRONOS mission: capability for sub-hourly synoptic observations of carbon monoxide and methane to quantify emissions and transport of air pollution D. Edwards et al. https://doi.org/10.5194/amt-11-1061-2018
148. Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. https://doi.org/10.1088/1748-9326/abd352
149. WRF-Chem simulations of CO2 over Belgium and surrounding countries assessed by ground-based measurements J. Wang et al. https://doi.org/10.5194/acp-26-3541-2026
150. The added value of satellite observations of methane forunderstanding the contemporary methane budget P. Palmer et al. https://doi.org/10.1098/rsta.2021.0106
151. Implications of Generation Efficiencies and Supply Chain Leaks for the Life Cycle Greenhouse Gas Emissions of Natural Gas-Fired Electricity in the United States S. Tavakkoli et al. https://doi.org/10.1021/acs.est.1c05246
152. Emissions of methane in Europe inferred by total column measurements D. Wunch et al. https://doi.org/10.5194/acp-19-3963-2019
153. U.S. CH4 emissions from oil and gas production: Have recent large increases been detected? L. Bruhwiler et al. https://doi.org/10.1002/2016JD026157
154. Methane emission reduction in China's natural gas industry: Construction of technology inventory and selection of optimal technology programs N. Li et al. https://doi.org/10.1016/j.spc.2023.12.002
155. Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. https://doi.org/10.5194/essd-15-3597-2023
156. Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements J. Worden et al. https://doi.org/10.5194/amt-8-3433-2015
157. Global inverse modeling of CH4 sources and sinks: an overview of methods S. Houweling et al. https://doi.org/10.5194/acp-17-235-2017
158. Estimating methane emissions from biological and fossil‐fuel sources in the San Francisco Bay Area S. Jeong et al. https://doi.org/10.1002/2016GL071794
159. Source Partitioning of Methane Emissions and its Seasonality in the U.S. Midwest Z. Chen et al. https://doi.org/10.1002/2017JG004356
160. Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations Y. Zhang et al. https://doi.org/10.5194/acp-21-3643-2021
161. Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere A. Ostler et al. https://doi.org/10.5194/amt-9-4843-2016
162. Quantifying sources of Brazil's CH4 emissions between 2010 and 2018 from satellite data R. Tunnicliffe et al. https://doi.org/10.5194/acp-20-13041-2020
163. Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020) M. Lunt et al. https://doi.org/10.5194/acp-21-16257-2021
164. Rates and pathways of sedimentary organic matter mineralization in two basins of a boreal lake: Emphasis on methanogenesis and methanotrophy F. Clayer et al. https://doi.org/10.1002/lno.10323
165. Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources K. Qin et al. https://doi.org/10.5194/acp-24-3009-2024
166. Global near-surface methane distribution and emission hotspots from TROPOMI: A geospatial machine-learning approach D. Lee et al. https://doi.org/10.1016/j.scitotenv.2026.181848
167. Contributions of the troposphere and stratosphere to CH4 model biases Z. Wang et al. https://doi.org/10.5194/acp-17-13283-2017
168. Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition H. Nesser et al. https://doi.org/10.5194/amt-14-5521-2021
169. Gridded National Inventory of U.S. Methane Emissions J. Maasakkers et al. https://doi.org/10.1021/acs.est.6b02878
170. Constraining sector-specific CO2 and CH4 emissions in the US S. Miller & A. Michalak https://doi.org/10.5194/acp-17-3963-2017
171. Atmospheric observation-based estimation of fossil fuel CO2 emissions from regions of central and southern California X. Cui et al. https://doi.org/10.1016/j.scitotenv.2019.01.081
172. Methane variability associated with natural and anthropogenic sources in an Australian context M. Hatch et al. https://doi.org/10.1080/08120099.2018.1471004
173. Quantifying methane emissions and assessing uncertainties in China's natural gas compressor stations W. Jia et al. https://doi.org/10.1016/j.jclepro.2026.148264
174. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations Z. Chen et al. https://doi.org/10.5194/acp-22-10809-2022
175. A decade of GOSAT Proxy satellite CH4 observations R. Parker et al. https://doi.org/10.5194/essd-12-3383-2020
176. Bottom-Up Estimates of Coal Mine Methane Emissions in China: A Gridded Inventory, Emission Factors, and Trends J. Sheng et al. https://doi.org/10.1021/acs.estlett.9b00294
177. Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON A. Liang et al. https://doi.org/10.3390/rs9101033
178. Atmospheric CO2 column concentration over Iran: Emissions, GOSAT satellite observations, and WRF-GHG model simulations S. Karbasi et al. https://doi.org/10.1016/j.atmosres.2024.107818
179. A Bayesian inversion of TROPOMI methane observations over South Africa: Implications for bottom-up inventories K. Maliehe et al. https://doi.org/10.1016/j.scitotenv.2026.181650
180. Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling S. Henne et al. https://doi.org/10.5194/acp-16-3683-2016
181. China’s coal mine methane regulations have not curbed growing emissions S. Miller et al. https://doi.org/10.1038/s41467-018-07891-7
182. 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. https://doi.org/10.5194/acp-21-4637-2021
183. Biogenic link to the recent increase in atmospheric methane over India A. Singh et al. https://doi.org/10.1016/j.jenvman.2021.112526
184. Five years of GOSAT-2 retrievals with RemoTeC: XCO2 and XCH4 data products with quality filtering by machine learning A. Barr et al. https://doi.org/10.5194/amt-18-6093-2025
185. Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet S. Sengupta & D. Cohan https://doi.org/10.1016/j.trd.2017.04.039
186. Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins L. Shen et al. https://doi.org/10.5194/acp-22-11203-2022
187. Methane, carbon dioxide, hydrogen sulfide, and isotopic ratios of methane observations from the Permian Basin tower network V. Monteiro et al. https://doi.org/10.5194/essd-14-2401-2022
188. Top-down constraints on global N2O emissions at optimal resolution: application of a new dimension reduction technique K. Wells et al. https://doi.org/10.5194/acp-18-735-2018
189. Enabling a Digital Earth for methane emissions management with equal-area discrete global grids M. Li & S. Liang https://doi.org/10.1080/17538947.2025.2607210
190. Landfill gas collection efficiency: Categorization of data from existing in-situ measurements C. Giordano et al. https://doi.org/10.1016/j.wasman.2023.12.042
191. Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling M. Johnson et al. https://doi.org/10.1002/2016JD025157
192. OOA-LightGBM: An Optimized Machine Learning Approach for Nationwide Seamless Reconstruction of XCH4 in China Z. Luo et al. https://doi.org/10.1007/s41748-026-01093-8
193. Investigation of monthly and seasonal changes of methane gas with respect to climate change using satellite data S. Javadinejad et al. https://doi.org/10.1007/s13201-019-1067-9
194. Methane emissions from the natural gas industry in China – A systematically accounting based on the bottom-up approach N. Li et al. https://doi.org/10.1016/j.jgsce.2024.205346
195. 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. https://doi.org/10.5194/acp-22-6811-2022
196. Non-intrusive soil carbon content quantification methods using machine learning algorithms: A comparison of microwave and millimeter wave radar sensors D. An & Y. Chen https://doi.org/10.1016/j.jai.2023.09.001
197. Observations of XCO2 and XCH4 with ground-based high-resolution FTS at Saga, Japan, and comparisons with GOSAT products H. Ohyama et al. https://doi.org/10.5194/amt-8-5263-2015
198. Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set M. Buchwitz et al. https://doi.org/10.1016/j.rse.2016.12.027
199. What can we learn about tropospheric OH from satellite observations of methane? E. Penn et al. https://doi.org/10.5194/acp-25-2947-2025
200. Satellite and ground-based measurements of XCO2 in a remote semiarid region of Australia V. Velazco et al. https://doi.org/10.5194/essd-11-935-2019
201. Influence of model vertical resolution on simulation of methane in the lowermost stratosphere Z. Wang et al. https://doi.org/10.1016/j.aosl.2026.100873
202. East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis R. Liang et al. https://doi.org/10.5194/acp-23-8039-2023
203. Atmospheric CH4 and CO2 enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes R. Parker et al. https://doi.org/10.5194/acp-16-10111-2016
204. Satellite-derived methane hotspot emission estimates using a fast data-driven method M. Buchwitz et al. https://doi.org/10.5194/acp-17-5751-2017
205. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. https://doi.org/10.5194/acp-22-9617-2022
206. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 J. Maasakkers et al. https://doi.org/10.5194/acp-19-7859-2019
207. Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space D. Cusworth et al. https://doi.org/10.5194/amt-12-5655-2019
208. Soil methane emission suppression control using unmanned aircraft vehicle swarm application of biochar mulch - A simulation study D. An et al. https://doi.org/10.1016/j.jiixd.2022.11.002
209. Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. https://doi.org/10.1029/2018GB006065
210. Spatio-Temporal Variability of Aerosol Optical Depth, Total Ozone and NO2 Over East Asia: Strategy for the Validation to the GEMS Scientific Products S. Park et al. https://doi.org/10.3390/rs12142256
211. An Approach to Estimate Atmospheric Greenhouse Gas Total Columns Mole Fraction from Partial Column Sampling J. Tadić & S. Biraud https://doi.org/10.3390/atmos9070247
212. Assessment of Regional Methane Emission Inventories through Airborne Quantification in the San Francisco Bay Area A. Guha et al. https://doi.org/10.1021/acs.est.0c01212
213. Accelerating methane growth rate from 2010 to 2017: leading contributions from the tropics and East Asia Y. Yin et al. https://doi.org/10.5194/acp-21-12631-2021
214. 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. https://doi.org/10.5194/acp-22-395-2022
215. SCIAMACHY observed changes in the column mixing ratio of methane over the Indian region and a comparison with global scenario M. Kavitha & P. Nair https://doi.org/10.1016/j.atmosenv.2017.07.044
216. New evidence for CH4 enhancement in the upper troposphere associated with the Asian summer monsoon M. Tao et al. https://doi.org/10.1088/1748-9326/ad2738
217. Remote‐Sensing Applications for Monitoring and Analysis of Waste Disposal Sites Using Satellite Images: A Review K. Sharma & A. Rajendra https://doi.org/10.1111/tgis.70175
218. High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China S. Feng et al. https://doi.org/10.5194/acp-25-15121-2025