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8. Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations P. Sadavarte et al. 10.1021/acs.est.1c03976
9. Balancing aggregation and smoothing errors in inverse models A. Turner & D. Jacob 10.5194/acp-15-7039-2015
10. Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations R. Janardanan et al. 10.3390/rs12030375
11. Estimating 2010–2015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations S. Baray et al. 10.5194/acp-21-18101-2021
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13. 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. 10.5194/amt-13-789-2020
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15. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. 10.3390/rs14020371
16. 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. 10.1016/j.scitotenv.2023.162222
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18. Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG A. Thorpe et al. 10.5194/amt-10-3833-2017
19. Detection of Methane Emission from a Local Source Using GOSAT Target Observations A. Kuze et al. 10.3390/rs12020267
20. Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories F. Wang et al. 10.3390/rs11212489
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25. Atmospheric Carbon and Transport – America (ACT‐America) Data Sets: Description, Management, and Delivery Y. Wei et al. 10.1029/2020EA001634
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27. Assimilation of satellite NO2 observations at high spatial resolution using OSSEs X. Liu et al. 10.5194/acp-17-7067-2017
28. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
29. Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign 2019 M. Makarova et al. 10.5194/amt-14-1047-2021
30. A 15-year record of CO emissions constrained by MOPITT CO observations Z. Jiang et al. 10.5194/acp-17-4565-2017
31. Comparing mass balance and adjoint methods for inverse modeling of nitrogen dioxide columns for global nitrogen oxide emissions M. Cooper et al. 10.1002/2016JD025985
32. Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions S. Ma et al. 10.1029/2021AV000408
33. 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
34. 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. 10.5194/gmd-14-7775-2021
35. Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010 S. Pandey et al. 10.5194/acp-16-5043-2016
36. A new aerial approach for quantifying and attributing methane emissions: implementation and validation J. Dooley et al. 10.5194/amt-17-5091-2024
37. 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. 10.5194/acp-16-12649-2016
38. Influences of Uncertainties in the STT Flux on Modeled Tropospheric Methane Z. Wang 10.1029/2023JD039107
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40. 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. 10.5194/acp-16-3227-2016
41. 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. 10.5194/acp-18-13881-2018
42. Mineralization of organic matter in boreal lake sediments: rates, pathways, and nature of the fermenting substrates F. Clayer et al. 10.5194/bg-17-4571-2020
43. Constraints on methane emissions in North America from future geostationary remote-sensing measurements N. Bousserez et al. 10.5194/acp-16-6175-2016
44. Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone B. Duncan et al. 10.1029/2019RG000652
45. An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data M. Lunt et al. 10.5194/acp-19-14721-2019
46. Comparison of Methane Detection Using Shortwave and Longwave Infrared Hyperspectral Sensors Under Varying Environmental Conditions L. Zimmerman & J. Kerekes 10.1109/JSTARS.2023.3247246
47. 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
48. 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. 10.5194/amt-11-6379-2018
49. Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources R. Bun et al. 10.1007/s11027-018-9791-2
50. 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. 10.5194/acp-16-15199-2016
51. The challenge of reconciling bottom-up agricultural methane emissions inventories with top-down measurements R. Desjardins et al. 10.1016/j.agrformet.2017.09.003
52. Methyl, Ethyl, and Propyl Nitrates: Global Distribution and Impacts on Reactive Nitrogen in Remote Marine Environments J. Fisher et al. 10.1029/2018JD029046
53. Rain-fed pulses of methane from East Africa during 2018–2019 contributed to atmospheric growth rate M. Lunt et al. 10.1088/1748-9326/abd8fa
54. Detecting high-emitting methane sources in oil/gas fields using satellite observations D. Cusworth et al. 10.5194/acp-18-16885-2018
55. Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra H. Oshio et al. 10.3390/rs12193155
56. Using Multiscale Ethane/Methane Observations to Attribute Coal Mine Vent Emissions in the San Juan Basin From 2013 to 2021 A. Meyer et al. 10.1029/2022JD037092
57. 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
58. Recent methane surges reveal heightened emissions from tropical inundated areas X. Lin et al. 10.1038/s41467-024-55266-y
59. 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. 10.5194/gmd-13-3839-2020
60. A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations S. Miller et al. 10.1002/2016GB005419
61. Evaluation of single-footprint AIRS CH4 profile retrieval uncertainties using aircraft profile measurements S. Kulawik et al. 10.5194/amt-14-335-2021
62. 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
63. 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. 10.5194/acp-18-12257-2018
64. On Methane Leaks from Pipelines in Bryan and College Station, Texas, USA K. Koch et al. 10.2174/1874282301610010056
65. The recent increase of atmospheric methane from 10 years of ground-based NDACC FTIR observations since 2005 W. Bader et al. 10.5194/acp-17-2255-2017
66. Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling R. Janardanan et al. 10.3390/rs9090941
67. Methane emissions are predominantly responsible for record-breaking atmospheric methane growth rates in 2020 and 2021 L. Feng et al. 10.5194/acp-23-4863-2023
68. Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales A. Turner et al. 10.5194/acp-18-8265-2018
69. 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
70. 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. 10.1016/j.scitotenv.2021.149433
71. 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. 10.5194/acp-21-17345-2021
72. Recovery of sparse urban greenhouse gas emissions B. Zanger et al. 10.5194/gmd-15-7533-2022
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74. 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
75. 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. 10.5194/acp-21-9545-2021
76. 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
77. A critical review of green growth indicators in G7 economies from 1990 to 2019 K. Herman et al. 10.1007/s11625-023-01397-y
78. Characterisation of methane variability and trends from near-infrared solar spectra over Hefei, China Y. Tian et al. 10.1016/j.atmosenv.2017.11.001
79. Retrieval of Metop-A/IASI N2O Profiles and Validation with NDACC FTIR Data B. Barret et al. 10.3390/atmos12020219
80. Conversion of surface CH4 concentrations from GOSAT satellite observations using XGBoost algorithm Y. Wan et al. 10.1016/j.atmosenv.2023.119694
81. Simultaneous shipborne measurements of CO2, CH4 and CO and their application to improving greenhouse-gas flux estimates in Australia B. Bukosa et al. 10.5194/acp-19-7055-2019
82. Methane emissions from dairies in the Los Angeles Basin C. Viatte et al. 10.5194/acp-17-7509-2017
83. MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery S. Jongaramrungruang et al. 10.1016/j.rse.2021.112809
84. A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico’s national inventory of greenhouse gases and compounds T. Scarpelli et al. 10.1088/1748-9326/abb42b
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88. Greenhouse gas column observations from a portable spectrometer in Uganda N. Humpage et al. 10.5194/amt-17-5679-2024
89. Tropospheric emissions: Monitoring of pollution (TEMPO) P. Zoogman et al. 10.1016/j.jqsrt.2016.05.008
90. Revisiting global fossil fuel and biofuel emissions of ethane Z. Tzompa‐Sosa et al. 10.1002/2016JD025767
91. Methane emissions from underground gas storage in California A. Thorpe et al. 10.1088/1748-9326/ab751d
92. Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases E. Solazzo et al. 10.5194/acp-21-5655-2021
93. An Unexpected Seasonal Cycle in U.S. Oil and Gas Methane Emissions L. Hu et al. 10.1021/acs.est.4c14090
94. Interpreting contemporary trends in atmospheric methane A. Turner et al. 10.1073/pnas.1814297116
95. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane Y. Zhang et al. 10.5194/acp-18-15959-2018
96. Cattle, climate and complexity: food security, quality and sustainability of the Australian cattle industries I. Lean & P. Moate 10.1111/avj.13072
97. 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
98. A simple and quick sensitivity analysis method for methane isotopologues detection with GOSAT-TANSO-FTS E. Malina et al. 10.14324/111.444/ucloe.000013
99. 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. 10.1021/acs.est.2c00927
100. Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. 10.1016/j.rse.2022.112966
101. Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions X. Yu et al. 10.5194/acp-21-951-2021
102. Large XCH4 anomaly in summer 2013 over northeast Asia observed by GOSAT M. Ishizawa et al. 10.5194/acp-16-9149-2016
103. Methane emissions from a waste treatment site: Numerical analysis of aircraft-based data Y. Cai et al. 10.1016/j.agrformet.2020.108102
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107. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space A. Kuze et al. 10.5194/amt-9-2445-2016
108. 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. 10.1029/2021JD034785
109. 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
110. 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
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113. 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. 10.1029/2018JD030183
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115. A Coupled CH4, CO and CO2 Simulation for Improved Chemical Source Modeling B. Bukosa et al. 10.3390/atmos14050764
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117. 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
118. Artificial Intelligence Approach for Estimating Dairy Methane Emissions S. Jeong et al. 10.1021/acs.est.1c08802
119. Improvements of a low-cost CO2 commercial nondispersive near-infrared (NDIR) sensor for unmanned aerial vehicle (UAV) atmospheric mapping applications Y. Liu et al. 10.5194/amt-15-4431-2022
120. 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
121. Seasonal variability of stratospheric methane: implications for constraining tropospheric methane budgets using total column observations K. Saad et al. 10.5194/acp-16-14003-2016
122. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. 10.3390/rs14020375
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129. 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. 10.5194/amt-11-1061-2018
130. Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
131. The added value of satellite observations of methane forunderstanding the contemporary methane budget P. Palmer et al. 10.1098/rsta.2021.0106
132. 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. 10.1021/acs.est.1c05246
133. Emissions of methane in Europe inferred by total column measurements D. Wunch et al. 10.5194/acp-19-3963-2019
134. U.S. CH4 emissions from oil and gas production: Have recent large increases been detected? L. Bruhwiler et al. 10.1002/2016JD026157
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136. 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. 10.5194/essd-15-3597-2023
137. Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements J. Worden et al. 10.5194/amt-8-3433-2015
138. Global inverse modeling of CH4 sources and sinks: an overview of methods S. Houweling et al. 10.5194/acp-17-235-2017
139. Estimating methane emissions from biological and fossil‐fuel sources in the San Francisco Bay Area S. Jeong et al. 10.1002/2016GL071794
140. Source Partitioning of Methane Emissions and its Seasonality in the U.S. Midwest Z. Chen et al. 10.1002/2017JG004356
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142. Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere A. Ostler et al. 10.5194/amt-9-4843-2016
143. Quantifying sources of Brazil's CH4 emissions between 2010 and 2018 from satellite data R. Tunnicliffe et al. 10.5194/acp-20-13041-2020
144. Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020) M. Lunt et al. 10.5194/acp-21-16257-2021
145. Rates and pathways of sedimentary organic matter mineralization in two basins of a boreal lake: Emphasis on methanogenesis and methanotrophy F. Clayer et al. 10.1002/lno.10323
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149. Gridded National Inventory of U.S. Methane Emissions J. Maasakkers et al. 10.1021/acs.est.6b02878
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152. Methane variability associated with natural and anthropogenic sources in an Australian context M. Hatch et al. 10.1080/08120099.2018.1471004
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154. A decade of GOSAT Proxy satellite CH4 observations R. Parker et al. 10.5194/essd-12-3383-2020
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156. Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON A. Liang et al. 10.3390/rs9101033
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162. Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet S. Sengupta & D. Cohan 10.1016/j.trd.2017.04.039
163. 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
164. 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
165. Top-down constraints on global N2O emissions at optimal resolution: application of a new dimension reduction technique K. Wells et al. 10.5194/acp-18-735-2018
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171. Non-intrusive soil carbon content quantification methods using machine learning algorithms: A comparison of microwave and millimeter wave radar sensors D. An & Y. Chen 10.1016/j.jai.2023.09.001
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