131 citations as recorded by crossref.

1. Spatial distribution pattern and long-term trend of atmospheric methane in the Atlantic-Mediterranean transition region based on TROPOMI and GOSAT measurements J. Adame et al. 10.1016/j.scitotenv.2024.178006
2. 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
3. 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
4. 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
5. 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
6. Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert 10.1038/s41467-024-47754-y
7. Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities S. Zhang et al. 10.1016/j.scitotenv.2023.164701
8. A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. 10.3389/fenvs.2024.1394281
9. First Investigation of Long-Term Methane Emissions from Wastewater Treatment Using Satellite Remote Sensing S. Mehrdad et al. 10.3390/rs16234422
10. S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. 10.1016/j.rse.2023.113708
11. Verifying Methane Inventories and Trends With Atmospheric Methane Data J. Worden et al. 10.1029/2023AV000871
12. Effect of Chicken Feather Hydrolysate on Growth and Yield of French Bean S. Devi et al. 10.1007/s12649-024-02532-1
13. Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2 D. Santaren et al. 10.5194/amt-18-211-2025
14. Response to Gallagher (2022)—the Australian Tidal Restoration for Blue Carbon method 2022—conservative, robust, and practical C. Lovelock et al. 10.1111/rec.14027
15. Methane and nitrous oxide emissions from municipal wastewater treatment plants in China: A plant-level and technology-specific study H. Li et al. 10.1016/j.ese.2023.100345
16. Geostationary satellite observations of extreme and transient methane emissions from oil and gas infrastructure M. Watine-Guiu et al. 10.1073/pnas.2310797120
17. Performance and sensitivity of column-wise and pixel-wise methane retrievals for imaging spectrometers A. Ayasse et al. 10.5194/amt-16-6065-2023
18. Space Imaging Point Source Detection and Characterization F. Ribeiro et al. 10.1109/ACCESS.2024.3420217
19. Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space A. Thorpe et al. 10.1126/sciadv.adh2391
20. Calibration of Maxar Constellation Over Libya-4 Site Using MAIAC Technique M. Choi et al. 10.1109/JSTARS.2024.3367250
21. Global observational coverage of onshore oil and gas methane sources with TROPOMI M. Gao et al. 10.1038/s41598-023-41914-8
22. 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
23. 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
24. Utilizing Remote Sensing and Data Analytics Techniques to Detect Methane Emissions from the Oil and Gas Industry and Assist with Sustainability Metrics Á. Esparza et al. 10.2118/215818-PA
25. Offshore methane detection and quantification from space using sun glint measurements with the GHGSat constellation J. MacLean et al. 10.5194/amt-17-863-2024
26. Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 10.1088/1748-9326/adad02
27. Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution M. Omara et al. 10.5194/essd-15-3761-2023
28. Single-photon laser methane detection methodology and initial validation S. Zhu et al. 10.3788/COL202422.100101
29. Satellite-Derived Estimate of City-Level Methane Emissions from Calgary, Alberta, Canada Z. Xing et al. 10.3390/rs16071149
30. Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. 10.1109/LGRS.2024.3379119
31. Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations J. Hakkarainen et al. 10.1016/j.rse.2025.114623
32. Uvsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases M. Meftah et al. 10.3390/rs15194876
33. U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. 10.5194/amt-17-2625-2024
34. High-resolution assessment of coal mining methane emissions by satellite in Shanxi, China S. Peng et al. 10.1016/j.isci.2023.108375
35. The role of satellite remote sensing in mitigating and adapting to global climate change S. Zhao et al. 10.1016/j.scitotenv.2023.166820
36. Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed C. Louime & T. Raza 10.3390/methane3030028
37. Accounting for the effect of aerosols in GHGSat methane retrieval Q. Yu et al. 10.5194/amt-17-3347-2024
38. Innovative software for analysing satellite data and methane emissions using radiative transfer model K. Aghayeva & G. Krauklit 10.62660/bcstu/3.2024.65
39. From planetary scenarios to planetary sensing: Models, observations, and political legibility D. Pendergrass 10.1177/20530196241270716
40. Single-blind validation of space-based point-source detection and quantification of onshore methane emissions E. Sherwin et al. 10.1038/s41598-023-30761-2
41. 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
42. Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data H. Wang et al. 10.3390/rs16061023
43. Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al. 10.3390/rs17040634
44. Air pollution accountability research: Moving from a chain to a web S. Ebelt et al. 10.1016/j.gloepi.2023.100128
45. 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
46. An Improved CH4 Profile Retrieving Method for Ground-Based Differential Absorption Lidar L. Fan et al. 10.3390/atmos15080937
47. Evaluation of Sentinel-5P TROPOMI Methane Observations at Northern High Latitudes H. Lindqvist et al. 10.3390/rs16162979
48. Current potential of CH4 emission estimates using TROPOMI in the Middle East M. Liu et al. 10.5194/amt-17-5261-2024
49. 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
50. 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
51. Constraining industrial ammonia emissions using hyperspectral infrared imaging L. Noppen et al. 10.1016/j.rse.2023.113559
52. Quantifying methane emissions from United States landfills D. Cusworth et al. 10.1126/science.adi7735
53. Tiered Leak Detection and Repair Programs at Simulated Oil and Gas Production Facilities: Increasing Emission Reduction by Targeting High-Emitting Sources F. Cardoso-Saldaña 10.1021/acs.est.2c08582
54. Investigation of U.S. landfill GHG reporting program methane emission models B. Stark et al. 10.1016/j.wasman.2024.05.037
55. Monitoring oil and gas field CH4 leaks by Sentinel-5P and Sentinel-2 B. He et al. 10.1016/j.fuel.2024.133889
56. Reconciling Methane Emission Measurements for Offshore Oil and Gas Platforms with Detailed Emission Inventories: Accounting for Emission Intermittency Z. Chen et al. 10.1021/acsenvironau.2c00041
57. Monitoring, classification and analysis of waste disposal sites using Machine Learning K. Sharma & M. Sood 10.1016/j.procs.2024.04.147
58. 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
59. A new aerial approach for quantifying and attributing methane emissions: implementation and validation J. Dooley et al. 10.5194/amt-17-5091-2024
60. 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
61. Temperature dependence of the absorption of the R(6) manifold of the 2ν3 band of methane in air in support of the MERLIN mission S. Vasilchenko et al. 10.1016/j.jqsrt.2023.108483
62. Evaluating net life-cycle greenhouse gas emissions intensities from gas and coal at varying methane leakage rates D. Gordon et al. 10.1088/1748-9326/ace3db
63. Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
64. 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
65. MAM-YOLOv9: A Multiattention Mechanism Network for Methane Emission Facility Detection in High-Resolution Satellite Remote Sensing Images Y. Xing et al. 10.1109/TGRS.2025.3545034
66. 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
67. Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan 10.3390/su162310400
68. Small emission sources in aggregate disproportionately account for a large majority of total methane emissions from the US oil and gas sector J. Williams et al. 10.5194/acp-25-1513-2025
69. Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas L. Fan et al. 10.3390/app142311100
70. Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions L. Yao et al. 10.3390/rs16234503
71. Bayesian inversion of HFC-134a emissions in southern China from a new AGAGE site: Results from an observing system simulation experiment J. Li et al. 10.1016/j.atmosenv.2024.120715
72. 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
73. CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. 10.5194/amt-17-2583-2024
74. Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. 10.1109/TGRS.2024.3523022
75. A framework for describing and classifying methane reporting requirements, emission sources, and monitoring methods A. Connor et al. 10.1039/D4EA00120F
76. Defining Detection Limits for Continuous Monitoring Systems for Methane Emissions at Oil and Gas Facilities Q. Chen et al. 10.3390/atmos15030383
77. Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants E. Kimei et al. 10.3390/su16052219
78. 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
79. Satellite monitoring of annual US landfill methane emissions and trends N. Balasus et al. 10.1088/1748-9326/ada2b1
80. Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al. 10.5194/amt-16-89-2023
81. Probability of Detection and Multi-Sensor Persistence of Methane Emissions from Coincident Airborne and Satellite Observations A. Ayasse et al. 10.1021/acs.est.4c06702
82. 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
83. 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
84. La surveillance des émissions anthropiques de CO2 depuis l’espace : un enjeu géopolitique émergent G. Broquet & F. Chevallier 10.4000/12de3
85. Deep learning for detecting and characterizing oil and gas well pads in satellite imagery N. Ramachandran et al. 10.1038/s41467-024-50334-9
86. Breakthroughs in CH4 capture technologies: Key to reducing fugitive methane emissions in the energy sector W. Deng et al. 10.1016/j.ccst.2024.100316
87. Advancing the Arctic Methane Permafrost Challenge (AMPAC) With Future Satellite Missions A. Bartsch et al. 10.1109/JSTARS.2025.3538897
88. Modeling global indices for estimating non-photosynthetic vegetation cover P. Dennison et al. 10.1016/j.rse.2023.113715
89. Investigation of spectral bands and sensor parameters for methane emission detection imaging spectrometer H. Varchand et al. 10.1016/j.jqsrt.2024.109100
90. Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches C. He et al. 10.1016/j.rse.2024.114192
91. Monitoring and regression analysis of landfill surface temperatures using remote sensing and image processing techniques K. Sharma et al. 10.1080/01431161.2024.2372081
92. PRISMethaNet: A novel deep learning model for landfill methane detection using PRISMA satellite data M. Marjani et al. 10.1016/j.isprsjprs.2024.10.003
93. Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al. 10.1016/j.isprsjprs.2025.03.020
94. 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
95. Evaluation of Uncertainties in the Anthropogenic SO2 Emissions in the USA from the OMI Point Source Catalog K. Narayan et al. 10.1021/acs.est.2c07056
96. Deployment-invariant probability of detection characterization for aerial LiDAR methane detection M. Thorpe et al. 10.1016/j.rse.2024.114435
97. 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
98. Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison G. McNicol et al. 10.1029/2023AV000956
99. Temporal Variation and Persistence of Methane Emissions from Shallow Water Oil and Gas Production in the Gulf of Mexico K. Biener et al. 10.1021/acs.est.3c08066
100. Quantitative imaging of carbon dioxide plumes using a ground-based shortwave infrared spectral camera M. Knapp et al. 10.5194/amt-17-2257-2024
101. The greenhouse gas observation mission with Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW): objectives, conceptual framework and scientific contributions H. Tanimoto et al. 10.1186/s40645-025-00684-9
102. Methane Column Estimation Using PRISMA Hyperspectral Data and Comparison With Other Earth Observation Products D. Settembre et al. 10.1109/LGRS.2025.3539870
103. Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications S. Ustin & E. Middleton 10.3390/s24113488
104. High Spatial and Temporal Resolution Simulations of Methane Column Loadings Due to Routine Emissions and Emission Events in Oil and Gas Regions L. Huang et al. 10.1021/acsestair.4c00021
105. Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. 10.3390/rs17030543
106. Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy V. Mayorova et al. 10.3390/s23156794
107. Practical paths towards quantifying and mitigating agricultural methane emissions E. Nisbet et al. 10.1098/rspa.2024.0390
108. 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
109. Comparison of observation- and inventory-based methane emissions for eight large global emitters A. Petrescu et al. 10.5194/essd-16-4325-2024
110. Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. 10.1021/acs.estlett.4c00399
111. Spatio-temporal dynamics of methane concentration and its association to climatic and vegetation parameters: a case study of the Northern Cape Province, South Africa N. Mashiyi et al. 10.1080/10106049.2024.2306266
112. A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B Z. He et al. 10.5194/amt-17-2937-2024
113. Towards verifying and improving estimations of China's CO2 and CH4 budgets using atmospheric inversions Y. Wang et al. 10.1093/nsr/nwaf090
114. Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions I. Katharopoulos et al. 10.5194/acp-23-14159-2023
115. Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
116. Forecasting short-term methane based on corrected numerical weather prediction outputs S. Zhao et al. 10.1016/j.jclepro.2024.142500
117. Exploring uncertainties in the integrated mass enhancement method for remote sensing retrievals of methane emissions M. Hasan et al. 10.1016/j.wasman.2025.114759
118. 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
119. US oil and gas system emissions from nearly one million aerial site measurements E. Sherwin et al. 10.1038/s41586-024-07117-5
120. Separating and quantifying facility-level methane emissions with overlapping plumes for spaceborne methane monitoring Y. Pang et al. 10.5194/amt-18-455-2025
121. 大气甲烷卫星传感器和遥感算法研究综述 何. He Zhuo et al. 10.3788/AOS230429
122. First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK E. Dowd et al. 10.5194/amt-17-1599-2024
123. Single-blind test of nine methane-sensing satellite systems from three continents E. Sherwin et al. 10.5194/amt-17-765-2024
124. Semantic segmentation of methane plumes with hyperspectral machine learning models V. Růžička et al. 10.1038/s41598-023-44918-6
125. Monitoring greenhouse gases (GHGs) in China: status and perspective Y. Sun et al. 10.5194/amt-15-4819-2022
126. Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
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128. 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
129. Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al. 10.3390/atmos16030279
130. Derivation of Emissions From Satellite‐Observed Column Amounts and Its Application to TROPOMI NO2 and CO Observations K. Sun 10.1029/2022GL101102
131. Recent Advances Toward Transparent Methane Emissions Monitoring: A Review B. Erland et al. 10.1021/acs.est.2c02136