81 citations as recorded by crossref.

1. A Multigranularity Spatiotemporal Attention Model Based on Multisource Satellite Data for Monthly XCO2 Reconstruction Over China R. Chen et al. https://doi.org/10.1109/TGRS.2025.3609734
2. A novel transformer-based CO2 retrieval framework incorporating prior constraint and hierarchical features injection: assessment of transferability for Tansat-2 L. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2026.01.039
3. Ai-driven carbon Monitoring: Transformer-Based reconstruction of atmospheric CO2 in Canadian poultry regions P. Prajesh et al. https://doi.org/10.1016/j.jag.2026.105248
4. Advantages of assimilating multispectral satellite retrievals of atmospheric composition: a demonstration using MOPITT carbon monoxide products W. Tang et al. https://doi.org/10.5194/amt-17-1941-2024
5. The Influence of Validation Colocation on XCO2 Satellite–Terrestrial Joint Observations R. Li et al. https://doi.org/10.3390/rs15225270
6. A global perspective on CO2 satellite observations in high AOD conditions T. Virtanen et al. https://doi.org/10.5194/amt-18-929-2025
7. Global daily 1 km gapless XCO₂ (2003−2023) derived from multi-satellite observations and a spatiotemporal deep learning framework J. Wang https://doi.org/10.1016/j.eiar.2025.108146
8. Extension of the Total Carbon Column Observing Network (TCCON) over the Eastern Mediterranean and Middle East: the Nicosia site in Cyprus C. Rousogenous et al. https://doi.org/10.5194/amt-19-565-2026
9. State of the Art in Monitoring Methane Emissions from Arctic–boreal Wetlands and Lakes M. Mahdianpari et al. https://doi.org/10.3390/rs18060926
10. 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
11. From Deterministic to Probabilistic: A Lightweight Framework for Probabilistic Machine Learning in Trace Gas Remote Sensing W. Chen et al. https://doi.org/10.34133/remotesensing.0881
12. 基于GEOS-Chem和多源卫星数据的全球CO2柱浓度同化分析 颜. Yan Xuezhi et al. https://doi.org/10.3788/AOS250644
13. MARVEL analysis of high-resolution rovibrational spectra of 17O13C18O and 17O13C17O A. Azzam et al. https://doi.org/10.1016/j.jqsrt.2025.109485
14. Global 20-year time series of XCO2 concentrations derived from satellite observations and interpolations to analyze XCO2 anomalies caused by wildfires W. Wefers et al. https://doi.org/10.1016/j.atmosres.2025.108408
15. Global CH4 fluxes derived from JAXA/GOSAT lower-tropospheric partial column data and the CarbonTracker Europe-CH4 atmospheric inverse model A. Tsuruta et al. https://doi.org/10.5194/acp-25-7829-2025
16. A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system Z. Jin et al. https://doi.org/10.5194/essd-16-2857-2024
17. Using a portable FTIR spectrometer to evaluate the consistency of Total Carbon Column Observing Network (TCCON) measurements on a global scale: the Collaborative Carbon Column Observing Network (COCCON) travel standard B. Herkommer et al. https://doi.org/10.5194/amt-17-3467-2024
18. A new global carbon flux estimation methodology by assimilation of both in situ and satellite CO2 observations W. Su et al. https://doi.org/10.1038/s41612-024-00824-w
19. Comparison of Landfill Methane Emission Quantification Using Multiple Observation Methods L. Gillespie et al. https://doi.org/10.1021/acsestair.5c00127
20. The added value of new ground-based observations in improving China's methane emission quantification H. Zhong et al. https://doi.org/10.5194/amt-19-4759-2026
21. Simulating Net Ecosystem Exchange of China’s Three Staple Food Crops and Their Responses to Heatwaves Y. Sun et al. https://doi.org/10.3390/rs18091399
22. The 626M24 dataset of validated transitions and empirical rovibrational energy levels of 16O12C16O A. Azzam et al. https://doi.org/10.1038/s41597-025-04755-w
23. Robust Spatio-temporal reconstruction of XCO₂ using OCO-2 and OCO-3 satellite observations and deep learning-based uncertainty quantification M. Awad & S. Homayouni https://doi.org/10.1016/j.nexres.2026.101707
24. Density dependence of measured line intensities for O2 transitions H. Tran et al. https://doi.org/10.1063/5.0302649
25. Analysis of the Current Situation of CO2 Satellite Observation Y. Li et al. https://doi.org/10.3390/rs17213635
26. Transformer-Based Fast Mole Fraction of CO 2 Retrievals from Satellite-Measured Spectra W. Chen et al. https://doi.org/10.34133/remotesensing.0470
27. The Greenhouse gas Emission Monitoring network to Inform Net-zero Initiatives UK (GEMINI-UK): network design, theoretical performance, and initial data A. Kurganskiy et al. https://doi.org/10.5194/amt-18-7525-2025
28. A high-resolution hourly XCO 2 dataset for eastern China (2016–2020) based on WRF-Chem simulations and multi-source validation W. Li & T. Ren https://doi.org/10.1080/20964471.2025.2585587
29. Contributions of argon, nitrogen, and oxygen to air broadening in the oxygen A-band L. Stevenson et al. https://doi.org/10.1016/j.jqsrt.2025.109480
30. Advancing Atmospheric Detection of Weakly Absorbing Reactive Trace Gases Using the FY-3E/HIRAS-II TIR Sounder on a Dawn–Dusk Orbit Z. Liang et al. https://doi.org/10.1021/acs.estlett.5c00501
31. Does the Satellite Sensing Column-Averaged Dry-Air Mole Fraction CO2 (XCO2) Require Oxygen Concentration From O2–A Band for Normalizing? L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3575603
32. WRF-Chem simulations of CO2 over Belgium and surrounding countries assessed by ground-based measurements J. Wang et al. https://doi.org/10.5194/acp-26-3541-2026
33. Annual growth rates of column-averaged CO2 inferred from Total Carbon Column Observing Network (TCCON) N. Mostafavi Pak et al. https://doi.org/10.5194/bg-23-1477-2026
34. Improved Data Processing and a Prior Profile Generation Method for Precise Retrieval of Atmospheric CO2 Based on a Laser Heterodyne Radiometer N. Fu et al. https://doi.org/10.3390/rs17233791
35. Time-series variation and prediction of near-surface atmospheric methane in the Hefei region based on TCCON a priori profile data J. Xu et al. https://doi.org/10.1016/j.jastp.2026.106904
36. The Harwell TCCON observatory D. Weidmann et al. https://doi.org/10.5194/gi-14-113-2025
37. A WRF-Chem study of the greenhouse gas column and in situ surface mole fractions observed at Xianghe, China – Part 2: Sensitivity of carbon dioxide (CO2) simulations to critical model parameters S. Callewaert et al. https://doi.org/10.5194/acp-26-899-2026
38. Local oscillator laser power locked laser heterodyne radiometer with variable optical attenuator Z. Chen et al. https://doi.org/10.1016/j.measurement.2026.121079
39. Disproportionately large impacts of wildland-urban interface fire emissions on global air quality and human health W. Tang et al. https://doi.org/10.1126/sciadv.adr2616
40. Long-term and interannual variations of atmospheric methane observed by the NIES and collaborative observation networks T. Umezawa et al. https://doi.org/10.1186/s40645-025-00711-9
41. Reconstructing long-term (2003–2019) global high-resolution XCO 2 : bridging observational gaps with machine learning S. Hwang et al. https://doi.org/10.1080/15481603.2026.2627042
42. An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data K. Hu et al. https://doi.org/10.3390/rs16111907
43. 温室气体多平台遥感探测研究（特邀） 刘. Liu Dong et al. https://doi.org/10.3788/AOS251794
44. Validation of Long-Term Methane (CH4) Satellite Measurements from the AIRS Spectrometer and Correction of Their Temporal Drift V. Rakitin et al. https://doi.org/10.3390/rs18081162
45. Global particulate pollution research reflects economic capacity more than pollution burden J. Yang et al. https://doi.org/10.1038/s44458-026-00126-7
46. Effects of Emission Variability on Atmospheric CO2 Concentrations in Mainland China W. Lu et al. https://doi.org/10.3390/rs17050814
47. Towards routine shipborne measurements of columnar CO2, CH4, CO, and NO2: a case study for tracking regional-scale emission patterns V. Enders et al. https://doi.org/10.5194/amt-19-2633-2026
48. Generating seamless hourly XCO2 maps over Asia: A CatBoost modeling approach integrating FY-4A GIIRS and OCO-2/3 (2021-2022) S. Borjigen et al. https://doi.org/10.1016/j.jclepro.2026.148493
49. 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. https://doi.org/10.5194/acp-25-4965-2025
50. Spatiotemporal bias in OCO-2 XCO2 retrievals relative to TCCON observations T. Nacson et al. https://doi.org/10.1016/j.jag.2026.105466
51. A retrieval of xCO2 from ground-based mid-infrared NDACC solar absorption spectra and comparison to TCCON R. Chiarella et al. https://doi.org/10.5194/amt-16-3987-2023
52. Achievements and Future Prospects of Greenhouse Gas Research for Climate Change Monitoring in Korea S. Joo et al. https://doi.org/10.5572/KOSAE.2025.41.2.281
53. Carbon emissions from the 2023 Canadian wildfires B. Byrne et al. https://doi.org/10.1038/s41586-024-07878-z
54. Review and perspective of remote sensing research on global greenhouse gas monitoring and stocktaking L. Liu et al. https://doi.org/10.1360/TB-2024-1033
55. 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. https://doi.org/10.1016/j.earscirev.2026.105493
56. Instrument Line Shape Calibration and Comparison with TCCON Measurements for Greenhouse Gas Monitoring at a New COCCON Site in Korea B. Trisna et al. https://doi.org/10.1007/s13143-025-00397-5
57. Fiducial Reference Measurement for Greenhouse Gases (FRM4GHG) M. Sha et al. https://doi.org/10.3390/rs16183525
58. 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
59. A WRF-Chem study of the greenhouse gas column and in situ surface concentrations observed in Xianghe, China – Part 1: Methane (CH4) S. Callewaert et al. https://doi.org/10.5194/acp-25-9519-2025
60. Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission C. Chan Miller et al. https://doi.org/10.5194/amt-17-5429-2024
61. Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions X. He et al. https://doi.org/10.3390/rs17244026
62. Quantifying CO emissions from boreal wildfires by assimilating TROPOMI and TCCON observations S. Voshtani et al. https://doi.org/10.5194/acp-25-15527-2025
63. Spatiotemporal Evolution of XCO2 in East Asia (2016–2024) Across Different Climate Zones Based on GOSAT and OCO-2 Data Fusion Z. Hu et al. https://doi.org/10.3390/rs18071004
64. CH4 and CO emission estimates for megacities: deriving enhancement ratios of CO2, CH4, and CO from GOSAT-2 observations H. Ohyama et al. https://doi.org/10.1088/1748-9326/ad89e0
65. Improving Model-Based Carbon Dioxide Datasets Using Deep Learning and Satellite Observations F. Mustafa & M. Xu https://doi.org/10.1109/TGRS.2025.3556309
66. Validation and assessment of satellite-based columnar CO2 and CH4 mixing ratios from GOSAT and OCO-2 satellites over India H. Gadhavi et al. https://doi.org/10.5194/amt-18-4497-2025
67. Assessment of the differences in European CH4 emission estimates from three TROPOMI products A. Sicsik-Paré et al. https://doi.org/10.5194/acp-26-10423-2026
68. XCO2 over East Asia: Assessing consistency, growth, and seasonality from multiple satellites and models Y. Yang et al. https://doi.org/10.1016/j.atmosres.2026.108974
69. Impact of anthropogenic emission inventories on atmospheric CO2 concentrations simulated by GEOS-Chem W. Lu et al. https://doi.org/10.1016/j.atmosres.2025.108255
70. 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
71. Principles of Correction for Long-Term Orbital Observations of Atmospheric Composition, Applied to AIRS v.6 CH4 and CO Data V. Rakitin et al. https://doi.org/10.3390/rs17132323
72. A Remote Sensing Technique for CO2 Column Density G. Hai et al. https://doi.org/10.1109/TGRS.2025.3579265
73. Two years of total column measurements of CO2, CH4 and CO in Paris, France J. Doc et al. https://doi.org/10.5194/acp-26-8937-2026
74. Reduction of airmass-dependent biases in TCCON XCH4 retrievals during polar vortex conditions J. Hachmeister et al. https://doi.org/10.5194/amt-18-7105-2025
75. Global 20-year XCO2 mapping through synergy of multi-satellite observations H. Yang et al. https://doi.org/10.1016/j.gsf.2026.102333
76. An Assessment of TROPESS CrIS and TROPOMI CO Retrievals and Their Synergies for the 2020 Western U.S. Wildfires O. Neyra-Nazarrett et al. https://doi.org/10.3390/rs17111854
77. Substantial Discrepancies Across Global Satellite XCO2 Products: A Systematic Evaluation J. Yang et al. https://doi.org/10.3390/rs18020371
78. An Improved CH4 Profile Retrieving Method for Ground-Based Differential Absorption Lidar L. Fan et al. https://doi.org/10.3390/atmos15080937
79. 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
80. Global Daily Column Average CO2 at 0.1° × 0.1° Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning F. Antezana Lopez et al. https://doi.org/10.1038/s41597-024-04135-w
81. An assessment of a CO2 transport model simulations using surface, aircraft and satellite data (2015–2021) C. Das et al. https://doi.org/10.1016/j.atmosenv.2025.121754