84 citations as recorded by crossref.

1. Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p S. Pandey et al.
2. Assessing Agricultural Methane Emissions and Temperature Feedbacks in the Loiret Region, France: A High-Resolution Sentinel-5P and Machine Learning Approach N. El Beyrouthy et al.
3. State of the Art in Monitoring Methane Emissions from Arctic–boreal Wetlands and Lakes M. Mahdianpari et al.
4. Global energy sector methane emissions estimated by using facility-level satellite observations D. Jervis et al.
5. Tracking NO2 and CO pollution hotspots at provincial scale in China with TROPOMI observations and image segmentation method M. Zeng et al.
6. Monthly methane emissions in Chinese mainland provinces from 2013–2022 D. Cui et al.
7. Global Identification of Solid Waste Methane Super Emitters Using Hyperspectral Satellites X. Zhang et al.
8. Assessing the Potential of the MTG-FCI Geostationary Mission for the Detection of Methane Plumes S. Zhou et al.
9. 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction P. Ebel et al.
10. Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite T. Lu et al.
11. Benzene and other hazardous air pollutants in consumer-grade natural gas in Europe T. Sparks et al.
12. Satellite monitoring of annual US landfill methane emissions and trends N. Balasus et al.
13. Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al.
14. S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al.
15. CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al.
16. GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al.
17. A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite S. Zhao et al.
18. TROPOMI/WFMD v2.0: Improved retrievals of XCH4 and XCO with XGBoost-based quality filtering O. Schneising et al.
19. Assessing the performance of emerging and existing continuous monitoring solutions under a single-blind controlled testing protocol F. Cheptonui et al.
20. Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak M. Dogniaux et al.
21. Remote‐Sensing Applications for Monitoring and Analysis of Waste Disposal Sites Using Satellite Images: A Review K. Sharma & A. Rajendra
22. FUMESNet: Exploring Frequency-Based Transformer and Improving Skip Connection for Hyperspectral Methane Plume Segmentation A. Dixit & P. Gupta
23. Study of collisional broadening and beyond-Voigt parameters of v4 methane lines broadened by helium using a high-resolution dual-comb spectrometer J. Clément et al.
24. 2019–2024 trends in African livestock and wetland emissions as contributors to the global methane rise N. Balasus et al.
25. The Carbon Mapper emissions monitoring system R. Duren et al.
26. Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al.
27. The methane imperative D. Shindell et al.
28. Review and perspective of remote sensing research on global greenhouse gas monitoring and stocktaking L. Liu et al.
29. Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du
30. 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.
31. Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al.
32. Dynamic fusion of medium-resolution optical and SAR imagery for methane source infrastructure classification Y. He et al.
33. CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al.
34. Tracking methane super-emitters from space J. O’Callaghan
35. A Novel Hybrid Quantum-Classical Path Optimization for Methane Detection Using Remote Quantum Intensity Prediction Models A. Prasad et al.
36. First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK E. Dowd et al.
37. Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2 D. Santaren et al.
38. 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.
39. An Intercomparison of Underground Coal Mine Methane Emission Estimation in Shanxi, China: S5P/TROPOMI vs. GF-5B/AHSI Z. Yang et al.
40. Long-term reconstruction of NO2 photolysis rate coefficients using machine learning and its impact on secondary pollution: A case study in a megacity of the Sichuan Basin, China T. Li et al.
41. Assessing methane emissions from collapsing Venezuelan oil production using TROPOMI B. Nathan et al.
42. CELNet: A comprehensive efficient learning network for atmospheric plume identification from remotely sensed methane concentration images F. Chen et al.
43. Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al.
44. Multi-scale weighted fusion network for hyperspectral and LiDAR data to identify stressed vegetation caused by natural gas storage microleakages K. Li et al.
45. Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al.
46. Estimating Methane Emissions by Integrating Satellite Regional Emissions Mapping and Point-Source Observations: Case Study in the Permian Basin M. Gao & Z. Xing
47. A critical analysis of challenges and opportunities for upcycling food waste to animal feed to reduce climate and resource burdens Z. Dou et al.
48. Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed C. Louime & T. Raza
49. Satellite-Derived Approaches for Coal Mine Methane Estimation: A Review A. Chauhan & S. Raval
50. Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al.
51. Multiplatform Methane Plume Detection via Model and Domain Adaptation V. Mancoridis et al.
52. High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China S. Bai et al.
53. Sentinel Data for Monitoring of Pollutant Emissions by Maritime Transport—A Literature Review T. Batista et al.
54. Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis S. Pandey et al.
55. Global inventory of doubly substituted isotopologues of methane (Δ13CH3D and Δ12CH2D2) S. Defratyka et al.
56. Measuring greenhouse gas emissions from composting: A comparative review of methods D. Dankwa et al.
57. Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al.
58. Advancing the Arctic Methane Permafrost Challenge (AMPAC) With Future Satellite Missions A. Bartsch et al.
59. Satellite Insights into methane Super-Emitters: Regional emissions and yearly growth on Turkmenistan’s west coast Z. He et al.
60. A helicopter-based mass balance approach for quantifying methane emissions from industrial activities, applied for coal mine ventilation shafts in Poland E. Förster et al.
61. Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al.
62. Evidence of animal productivity outcomes when fed diets including food waste: A systematic review of global primary data Y. Wang et al.
63. Long-term investigation of methane and carbon dioxide emissions in two Italian landfills L. Brilli et al.
64. Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al.
65. Artificial intelligence‐driven insights: Precision tracking of power plant carbon emissions using satellite data Z. Zhang et al.
66. SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al.
67. Not just a climate problem: the safety and health risks of methane super-emitter events S. Bisogno et al.
68. Satellites in addressing climate change: Trends, challenges, and future directions G. Zhou et al.
69. 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.
70. AutoMergeNet: AutoML-Based M-Source Satellite Data Fusion Evaluated With Atmospheric Case Studies J. Wąsala et al.
71. Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert
72. 温室气体多平台遥感探测研究（特邀） 刘. Liu Dong et al.
73. Lab-on-fiber: Highly sensitive methane sensor based on ZIF-8/PDMS functionalized Fabry‐Perot interferometer R. Zhou et al.
74. Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al.
75. Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations J. Hakkarainen et al.
76. Identifying and quantifying greenhouse gas emissions with the AVIRIS-3 airborne imaging spectrometer R. Coleman et al.
77. Global satellite survey reveals uncertainty in landfill methane emissions M. Dogniaux et al.
78. Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions F. Arkian et al.
79. Identification of false methane plumes for orbital imaging spectrometers: A case study with EMIT C. Xiang et al.
80. Surveying methane point-source super-emissions across oil and gas basins with MethaneSAT L. Guanter et al.
81. Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al.
82. Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates J. East et al.
83. Detection and quantification of large fugitive methane emissions in the Madrid region using ambient concentration measurements and dispersion modeling A. Domínguez-Sáez et al.
84. Ten new insights in climate science 2024 R. Schaeffer et al.