117 citations as recorded by crossref.

1. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations Z. Chen et al.
2. Spatiotemporal decoupling CH4 emission from economic growth and future trend in categorized Chinese cities C. Xu et al.
3. Global Methane Budget 2000–2020 M. Saunois et al.
4. How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions B. Zheng et al.
5. Global observational coverage of onshore oil and gas methane sources with TROPOMI M. Gao et al.
6. Estimating Methane Emissions by Integrating Satellite Regional Emissions Mapping and Point-Source Observations: Case Study in the Permian Basin M. Gao & Z. Xing
7. 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.
8. Simulation and Error Analysis of Methane Detection Globally Using Spaceborne IPDA Lidar X. Zhang et al.
9. 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. 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.
11. Reconciling Bottom–Up and Top–Down Approaches to Quantify Sub-Regional Methane Emissions with Improved Inventory and Three-Year High-Resolution Satellite Measurements C. He et al.
12. Derivation of Emissions From Satellite‐Observed Column Amounts and Its Application to TROPOMI NO2 and CO Observations K. Sun
13. A Bayesian inversion of TROPOMI methane observations over South Africa: Implications for bottom-up inventories K. Maliehe et al.
14. Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al.
15. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes D. Cusworth et al.
16. Natural Gas Leakage Ratio Determined from Flux Measurements of Methane in Urban Beijing Y. Huangfu et al.
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.
18. Mapping U.S. Surface Methane With Artificial Neural Networks M. Bade & Y. Li
19. Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics X. Lu et al.
20. Gap-filled spatiotemporal reconstruction of XCH4 data and analysis of methane emission patterns Q. Xiao et al.
21. Methane, carbon dioxide, hydrogen sulfide, and isotopic ratios of methane observations from the Permian Basin tower network V. Monteiro et al.
22. Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al.
23. Estimating ground-level CH4 concentrations inferred from Sentinel-5P J. Qin et al.
24. Monitoring greenhouse gases (GHGs) in China: status and perspective Y. Sun et al.
25. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations L. Shen et al.
26. Why methane surged in the atmosphere during the early 2020s P. Ciais et al.
27. What can we learn about tropospheric OH from satellite observations of methane? E. Penn et al.
28. Integrating Satellite Observations and Hydrological Models to Unravel Large TROPOMI Methane Emissions in South Sudan Wetlands Y. Albuhaisi et al.
29. Emerging Trends, Sectoral, and Regional Patterns in China’s Methane Emissions: A Satellite-Constrained Perspective D. Chen et al.
30. 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.
31. Local and regional enhancements of CH4, CO, and CO2 inferred from TCCON column measurements K. Mottungan et al.
32. Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al.
33. Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations D. Pendergrass et al.
34. A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al.
35. Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al.
36. Assessing the Potential of the MTG-FCI Geostationary Mission for the Detection of Methane Plumes S. Zhou et al.
37. Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al.
38. Emissions of climate-altering species from open vegetation fires in the Mediterranean region - A review on methods and data R. Hundal et al.
39. Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India T. Mathew et al.
40. 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.
41. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al.
42. Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al.
43. Monthly and annual oscillation of methane and ozone in eastern India using Sentinel-5P TROPOMI: assessment and validation A. Ghosh & S. Manna
44. Global Estimation of Surface Methane Using Low-Earth Orbit Satellite Data and Machine Learning: Comparison of Linear and Nonlinear Models D. Lee et al.
45. Verifying Methane Inventories and Trends With Atmospheric Methane Data J. Worden et al.
46. Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al.
47. CHN-CH4: a gridded (0.1° × 0.1°) anthropogenic methane emission inventory of China from 1990 to 2020 F. Guo et al.
48. Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al.
49. Quantifying Methane–Climate Interactions in Eastern Saudi Arabia Using Geospatial and Machine Learning Modeling M. Rahman et al.
50. 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.
51. 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.
52. Declining short-term emission control opportunity for major events in Chinese cities H. Wang et al.
53. Evaluation of the methane full-physics retrieval applied to TROPOMI ocean sun glint measurements A. Lorente et al.
54. Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al.
55. Assessment of methane emissions from oil, gas and coal sectors across inventories and atmospheric inversions K. Tibrewal et al.
56. Methane intensity and emissions across major oil and gas basins and individual jurisdictions using MethaneSAT observations J. Williams et al.
57. Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al.
58. Using portable low-resolution spectrometers to evaluate Total Carbon Column Observing Network (TCCON) biases in North America N. Mostafavi Pak et al.
59. Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge Z. Qu et al.
60. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al.
61. Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al.
62. 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.
63. Evaluation of model-simulated atmospheric methane over South Korea using ground-based and aircraft observations S. Do et al.
64. Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater B. de Foy et al.
65. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al.
66. Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States D. Cusworth et al.
67. Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al.
68. High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China S. Feng et al.
69. Tracking regional CH4 emissions through collocated air pollution measurement: a pilot application and robustness analysis in China Y. Li & B. Zheng
70. Methane emissions from the oil and gas supply chain: Characteristics and mitigation H. Lu et al.
71. Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al.
72. Calibration and enhancement of an intensity-coded NIR laser single-photon system for quantitative methane leak monitoring S. Zhu et al.
73. 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.
74. 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.
75. Detection of local-source contributions in ground-level atmospheric methane in Japan J. Zhang & H. Hayami
76. Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion J. McNorton et al.
77. Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al.
78. Estimated regional CO2 flux and uncertainty based on an ensemble of atmospheric CO2 inversions N. Chandra et al.
79. A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management N. Parazoo et al.
80. Assessing the impact of natural factors on CH₄ concentrations in Chinese cities from 2009 to 2023 Z. Linjing et al.
81. Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al.
82. North–south inhomogeneous variations of methane emissions observed by TROPOMI over China S. Yu et al.
83. Retrieving the Vertical Profile of Greenhouse Gas Using Fourier Transform Spectrometer (FTS) - Part II: Methane (CH4) M. Kim et al.
84. Co-benefits for net carbon emissions and rice yields through improved management of organic nitrogen and water B. Liu et al.
85. Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites Q. Ni et al.
86. 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.
87. Satellite-Based Monitoring of Methane Emissions from China’s Rice Hub R. Liang et al.
88. Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission C. Chan Miller et al.
89. Uncertainty and retrieval sensitivity in TROPOMI-based methane inversions over the North Slope of Alaska R. Ward et al.
90. Cold‐Season Methane Fluxes Simulated by GCP‐CH4 Models A. Ito et al.
91. The Spatial and Temporal Distribution Patterns of XCH4 in China: New Observations from TROPOMI J. Zhang et al.
92. Quantifying Methane Emissions Using Satellite Data: Application of the Integrated Methane Inversion (IMI) Model to Assess Danish Emissions A. Vara-Vela et al.
93. The integration of vision transformers and SAM for automated methane super-emitter detection using TROPOMI data M. Marjani et al.
94. Satellite-Derived Estimates on Methane Emissions From Rice Paddies Across China X. Zhang et al.
95. Global CH4 fluxes derived from JAXA/GOSAT lower-tropospheric partial column data and the CarbonTracker Europe-CH4 atmospheric inverse model A. Tsuruta et al.
96. 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.
97. Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al.
98. Assessing methane emissions from collapsing Venezuelan oil production using TROPOMI B. Nathan et al.
99. A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources X. Yu et al.
100. Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method Q. Tu et al.
101. Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al.
102. 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.
103. 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.
104. East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis R. Liang et al.
105. A Review of City-Scale Methane Flux Inversion Based on Top-Down Methods X. Li et al.
106. Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions S. Zhu et al.
107. Regenerative rice farming for sustaining productivity, reducing energy demand, and methane emissions in India: A comprehensive review G. Sawargaonkar et al.
108. Investigation of China’s Anthropogenic Methane Emissions with Approaches, Potentials, Economic Cost, and Social Benefits of Reductions R. Feng et al.
109. 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.
110. Methane emission and influencing factors of China's oil and natural gas sector in 2020–2060: A source level analysis S. Sun et al.
111. Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations D. Varon et al.
112. Monthly methane emissions in Chinese mainland provinces from 2013–2022 D. Cui et al.
113. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al.
114. Bridging the Gap: A comprehensive review and cross-check analysis for China's Non-CO2 greenhouse gas emission estimates L. Hu & X. Fang
115. 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.
116. A Method to Estimate Clear-Sky Albedo of Paddy Rice Fields T. Sun et al.
117. 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.