71 citations as recorded by crossref.

1. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data A. Turner et al. https://doi.org/10.5194/acp-15-7049-2015
2. Atmospheric CH4 and CO2 enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes R. Parker et al. https://doi.org/10.5194/acp-16-10111-2016
3. Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. https://doi.org/10.5194/acp-16-14371-2016
4. Analysis of total column CO2 and CH4 measurements in Berlin with WRF-GHG X. Zhao et al. https://doi.org/10.5194/acp-19-11279-2019
5. Methane profiles from GOSAT thermal infrared spectra A. de Lange & J. Landgraf https://doi.org/10.5194/amt-11-3815-2018
6. Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Z. Qu et al. https://doi.org/10.5194/acp-21-14159-2021
7. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. https://doi.org/10.3390/rs14020371
8. Atmospheric methane retrieval and validation using FY-3F/ HIRAS-II observations W. Fang et al. https://doi.org/10.1016/j.atmosres.2026.108838
9. The MUSICA IASI CH4 and N2O products and their comparison to HIPPO, GAW and NDACC FTIR references O. García et al. https://doi.org/10.5194/amt-11-4171-2018
10. Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? Z. Tan et al. https://doi.org/10.5194/acp-16-12649-2016
11. Assessing 5 years of GOSAT Proxy XCH4 data and associated uncertainties R. Parker et al. https://doi.org/10.5194/amt-8-4785-2015
12. Accelerating methane growth rate from 2010 to 2017: leading contributions from the tropics and East Asia Y. Yin et al. https://doi.org/10.5194/acp-21-12631-2021
13. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane Y. Zhang et al. https://doi.org/10.5194/acp-18-15959-2018
14. MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane G. Ehret et al. https://doi.org/10.3390/rs9101052
15. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. https://doi.org/10.1088/1748-9326/ac8754
16. Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources K. Qin et al. https://doi.org/10.5194/acp-24-3009-2024
17. The Spatial and Temporal Distribution Patterns of XCH4 in China: New Observations from TROPOMI J. Zhang et al. https://doi.org/10.3390/atmos13020177
18. Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations R. Parker et al. https://doi.org/10.5194/bg-17-5669-2020
19. The global methane budget 2000–2012 M. Saunois et al. https://doi.org/10.5194/essd-8-697-2016
20. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 J. Maasakkers et al. https://doi.org/10.5194/acp-19-7859-2019
21. Assessment of seasonal variations of carbon dioxide concentration in Iran using GOSAT data S. Mousavi et al. https://doi.org/10.1111/1477-8947.12121
22. Accounting for Transport Error in Inversions: An Urban Synthetic Data Experiment S. Ghosh et al. https://doi.org/10.1029/2020EA001272
23. Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge Z. Qu et al. https://doi.org/10.1073/pnas.2402730121
24. Satellite-derived methane hotspot emission estimates using a fast data-driven method M. Buchwitz et al. https://doi.org/10.5194/acp-17-5751-2017
25. Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere A. Ostler et al. https://doi.org/10.5194/amt-9-4843-2016
26. Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN S. Falahatkar et al. https://doi.org/10.1007/s10661-017-6285-8
27. Wetland emission and atmospheric sink changes explain methane growth in 2020 S. Peng et al. https://doi.org/10.1038/s41586-022-05447-w
28. Assimilation of multiple datasets results in large differences in regional- to global-scale NEE and GPP budgets simulated by a terrestrial biosphere model C. Bacour et al. https://doi.org/10.5194/bg-20-1089-2023
29. Recent methane surges reveal heightened emissions from tropical inundated areas X. Lin et al. https://doi.org/10.1038/s41467-024-55266-y
30. East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis R. Liang et al. https://doi.org/10.5194/acp-23-8039-2023
31. Error Budget of the MEthane Remote LIdar missioN and Its Impact on the Uncertainties of the Global Methane Budget P. Bousquet et al. https://doi.org/10.1029/2018JD028907
32. Development and Validation of an End-to-End Simulator and Gas Concentration Retrieval Processor Applied to the MERLIN Lidar Mission V. Cassé et al. https://doi.org/10.3390/rs13142679
33. Constraints on methane emissions in North America from future geostationary remote-sensing measurements N. Bousserez et al. https://doi.org/10.5194/acp-16-6175-2016
34. Global inverse modeling of CH4 sources and sinks: an overview of methods S. Houweling et al. https://doi.org/10.5194/acp-17-235-2017
35. Diagnostic methods for atmospheric inversions of long-lived greenhouse gases A. Michalak et al. https://doi.org/10.5194/acp-17-7405-2017
36. Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set M. Buchwitz et al. https://doi.org/10.1016/j.rse.2016.12.027
37. Influences of hydroxyl radicals (OH) on top-down estimates of the global and regional methane budgets Y. Zhao et al. https://doi.org/10.5194/acp-20-9525-2020
38. What can we learn about tropospheric OH from satellite observations of methane? E. Penn et al. https://doi.org/10.5194/acp-25-2947-2025
39. Interpreting contemporary trends in atmospheric methane A. Turner et al. https://doi.org/10.1073/pnas.1814297116
40. The Monitoring Nitrous Oxide Sources (MIN2OS) satellite project P. Ricaud et al. https://doi.org/10.1016/j.rse.2021.112688
41. The challenge of reconciling bottom-up agricultural methane emissions inventories with top-down measurements R. Desjardins et al. https://doi.org/10.1016/j.agrformet.2017.09.003
42. Where to place methane monitoring sites in China to better assist carbon management X. Zhang et al. https://doi.org/10.1038/s41612-023-00359-6
43. Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. https://doi.org/10.3390/atmos14040758
44. Natural and anthropogenic methane fluxes in Eurasia: a mesoscale quantification by generalized atmospheric inversion A. Berchet et al. https://doi.org/10.5194/bg-12-5393-2015
45. Inverse modelling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY M. Alexe et al. https://doi.org/10.5194/acp-15-113-2015
46. Objectified quantification of uncertainties in Bayesian atmospheric inversions A. Berchet et al. https://doi.org/10.5194/gmd-8-1525-2015
47. Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH4 observations R. Parker et al. https://doi.org/10.1016/j.rse.2018.02.011
48. On the use of satellite-derived CH4 : CO2 columns in a joint inversion of CH4 and CO2 fluxes S. Pandey et al. https://doi.org/10.5194/acp-15-8615-2015
49. A nitrate photolysis source of tropospheric HONO is incompatible with current understanding of atmospheric chemistry M. Rowlinson et al. https://doi.org/10.5194/acp-25-16945-2025
50. Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins L. Shen et al. https://doi.org/10.5194/acp-22-11203-2022
51. Variability and quasi-decadal changes in the methane budget over the period 2000–2012 M. Saunois et al. https://doi.org/10.5194/acp-17-11135-2017
52. Large XCH4 anomaly in summer 2013 over northeast Asia observed by GOSAT M. Ishizawa et al. https://doi.org/10.5194/acp-16-9149-2016
53. Eight-Year Estimates of Methane Emissions from Oil and Gas Operations in Western Canada Are Nearly Twice Those Reported in Inventories E. Chan et al. https://doi.org/10.1021/acs.est.0c04117
54. Biogenic link to the recent increase in atmospheric methane over India A. Singh et al. https://doi.org/10.1016/j.jenvman.2021.112526
55. 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. https://doi.org/10.1016/j.jqsrt.2023.108483
56. Decadal trends in global CO emissions as seen by MOPITT Y. Yin et al. https://doi.org/10.5194/acp-15-13433-2015
57. Can we detect regional methane anomalies? A comparison between three observing systems C. Cressot et al. https://doi.org/10.5194/acp-16-9089-2016
58. Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales A. Turner et al. https://doi.org/10.5194/acp-18-8265-2018
59. Sensitivity of the recent methane budget to LMDz sub-grid-scale physical parameterizations R. Locatelli et al. https://doi.org/10.5194/acp-15-9765-2015
60. Long-Term Trends and Spatiotemporal Variations in Atmospheric XCH4 over China Utilizing Satellite Observations J. Xu et al. https://doi.org/10.3390/atmos13040525
61. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations L. Shen et al. https://doi.org/10.1038/s41467-023-40671-6
62. Spatiotemporal variability of methane over the Amazon from satellite observations I. Ribeiro et al. https://doi.org/10.1007/s00376-016-5138-7
63. Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra H. Oshio et al. https://doi.org/10.3390/rs12193155
64. Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations Y. Zhang et al. https://doi.org/10.5194/acp-21-3643-2021
65. Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model I. Stanevich et al. https://doi.org/10.5194/gmd-13-3839-2020
66. TCCON Philippines: First Measurement Results, Satellite Data and Model Comparisons in Southeast Asia V. Velazco et al. https://doi.org/10.3390/rs9121228
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. A decade of GOSAT Proxy satellite CH4 observations R. Parker et al. https://doi.org/10.5194/essd-12-3383-2020
69. Strategic design of methane observation networks to improve emission estimates: A case study in Africa H. Li et al. https://doi.org/10.5194/acp-26-11893-2026
70. Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations X. Lu et al. https://doi.org/10.5194/acp-21-4637-2021
71. An integrated analysis of contemporary methane emissions and concentration trends over China using in situ and satellite observations and model simulations H. Tan et al. https://doi.org/10.5194/acp-22-1229-2022