47 citations as recorded by crossref.

1. Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions S. Zhu et al. https://doi.org/10.3390/rs17132321
2. Global Daily Atmospheric Methane Column Concentration Retrieval From TROPOMI Satellite Spectral Measurements by Two-Stage Machine Learning Algorithm Y. Bao et al. https://doi.org/10.1109/JSTARS.2026.3688284
3. MCF-XCO2: A cross-mission consistency and fusion framework for integrating multi-satellite XCO2 observations Y. Yu et al. https://doi.org/10.1016/j.atmosres.2026.108747
4. Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning H. Bi & S. Neethirajan https://doi.org/10.3390/cli12120223
5. 基于地面与机载测量的青藏高原CO<sub>2</sub>与CH<sub>4</sub>浓度助力卫星数据验证初探 宜. 汪 & 向. 田 https://doi.org/10.1360/N072025-0040
6. Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. https://doi.org/10.5194/acp-25-797-2025
7. Emerging Trends, Sectoral, and Regional Patterns in China’s Methane Emissions: A Satellite-Constrained Perspective D. Chen et al. https://doi.org/10.1021/acsestair.5c00405
8. Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. https://doi.org/10.1016/j.jenvman.2025.126309
9. Multi-modal neural fusion for accurate carbon dioxide column sensing using laser heterodyne radiometry H. Xiong et al. https://doi.org/10.1016/j.snb.2026.139973
10. Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction A. Bradley et al. https://doi.org/10.5194/amt-18-1675-2025
11. Satellite monitoring of annual US landfill methane emissions and trends N. Balasus et al. https://doi.org/10.1088/1748-9326/ada2b1
12. Satellite-Derived Estimates on Methane Emissions From Rice Paddies Across China X. Zhang et al. https://doi.org/10.1109/TGRS.2026.3665059
13. Correction of near-surface methane concentrations using a CNN-RF hybrid model based on multi-scale feature extraction L. Fan et al. https://doi.org/10.1016/j.apr.2026.103078
14. Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
15. Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates J. East et al. https://doi.org/10.1038/s41467-025-67122-8
16. 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
17. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
18. Gap-filled spatiotemporal reconstruction of XCH4 data and analysis of methane emission patterns Q. Xiao et al. https://doi.org/10.1016/j.apr.2026.102918
19. Global daily TROPOMI XCH₄ reconstruction and methane emission hotspot identification using machine learning Q. Xiao et al. https://doi.org/10.1080/17538947.2026.2677964
20. A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. https://doi.org/10.5194/amt-17-5147-2024
21. 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. https://doi.org/10.1016/j.scitotenv.2024.178006
22. Bridging the gap: Ground-based and airborne measurements of CO2 and CH4 over the Tibetan Plateau for satellite validation Y. Wang & X. Tian https://doi.org/10.1007/s11430-025-1553-9
23. Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
24. Duration of super-emitting oil and gas methane sources D. Cusworth et al. https://doi.org/10.1038/s41467-026-68804-7
25. Seasonality and Declining Intensity of Methane Emissions from the Permian and Nearby US Oil and Gas Basins D. Varon et al. https://doi.org/10.1021/acs.est.5c08745
26. TROPOMI/WFMD v2.0: Improved retrievals of XCH4 and XCO with XGBoost-based quality filtering O. Schneising et al. https://doi.org/10.5194/amt-19-2407-2026
27. Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe R. Padilla et al. https://doi.org/10.1016/j.atmosenv.2024.120665
28. Retrieval of Atmospheric XCH4 via XGBoost Method Based on TROPOMI Satellite Data W. Zhang et al. https://doi.org/10.3390/atmos16030279
29. 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
30. Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
31. Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. https://doi.org/10.1016/j.rse.2024.114039
32. 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. https://doi.org/10.5194/gmd-18-3311-2025
33. Review of methane emission source tracing methods in oilfield regions Y. Liu et al. https://doi.org/10.1016/j.jgsce.2025.205708
34. Innovative software for analysing satellite data and methane emissions using radiative transfer model K. Aghayeva & G. Krauklit https://doi.org/10.62660/bcstu/3.2024.65
35. Quantifying methane emission baselines with high-resolution satellite data to support China’s emission control H. Zhong et al. https://doi.org/10.1016/j.scib.2025.04.047
36. Quantifying urban and landfill methane emissions in the United States using TROPOMI satellite data X. Wang et al. https://doi.org/10.1126/sciadv.adz9308
37. Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan https://doi.org/10.3390/su162310400
38. Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
39. How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions B. Zheng et al. https://doi.org/10.5194/acp-26-1931-2026
40. Attributing 2019–2024 methane growth using TROPOMI satellite observations M. He et al. https://doi.org/10.1126/sciadv.adz9007
41. Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. https://doi.org/10.3390/rs17030543
42. 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
43. State of the Art in Monitoring Methane Emissions from Arctic–boreal Wetlands and Lakes M. Mahdianpari et al. https://doi.org/10.3390/rs18060926
44. 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
45. Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT T. Cai & C. Xiang https://doi.org/10.3390/atmos15030266
46. 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. https://doi.org/10.5194/acp-25-14353-2025
47. 2019–2024 trends in African livestock and wetland emissions as contributors to the global methane rise N. Balasus et al. https://doi.org/10.5194/acp-26-4601-2026