131 citations as recorded by crossref.

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11. Methane Emission From Global Lakes: New Spatiotemporal Data and Observation‐Driven Modeling of Methane Dynamics Indicates Lower Emissions M. Johnson et al. https://doi.org/10.1029/2022JG006793
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14. Seasonal and interannual variability in wetland methane emissions simulated by CLM4Me' and CAM-chem and comparisons to observations of concentrations L. Meng et al. https://doi.org/10.5194/bg-12-4029-2015
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20. Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
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25. A satellite data driven biophysical modeling approach for estimating northern peatland and tundra CO2 and CH4 fluxes J. Watts et al. https://doi.org/10.5194/bg-11-1961-2014
26. Methane emissions from global rice fields: Magnitude, spatiotemporal patterns, and environmental controls B. Zhang et al. https://doi.org/10.1002/2016GB005381
27. Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100 J. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.177169
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33. Impacts of climate and reclamation on temporal variations in CH4 emissions from different wetlands in China: from 1950 to 2010 T. Li et al. https://doi.org/10.5194/bg-12-6853-2015
34. Carbon cycle uncertainty in the Alaskan Arctic J. Fisher et al. https://doi.org/10.5194/bg-11-4271-2014
35. The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas J. Bernard et al. https://doi.org/10.5194/essd-17-2985-2025
36. Effects of temperature and pH on archaeal membrane lipid distributions in freshwater wetlands J. Blewett et al. https://doi.org/10.1016/j.orggeochem.2020.104080
37. Can we explain the observed methane variability after the Mount Pinatubo eruption? N. Bândă et al. https://doi.org/10.5194/acp-16-195-2016
38. Modeling CH4 Emissions from Natural Wetlands on the Tibetan Plateau over the Past 60 Years: Influence of Climate Change and Wetland Loss T. Li et al. https://doi.org/10.3390/atmos7070090
39. 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
40. Areal extent of vegetative cover: A challenge to regional upscaling of methane emissions J. Melack & L. Hess https://doi.org/10.1016/j.aquabot.2022.103592
41. Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0) J. Bernard et al. https://doi.org/10.5194/gmd-18-863-2025
42. Modeling micro-topographic controls on boreal peatland hydrology and methane fluxes F. Cresto Aleina et al. https://doi.org/10.5194/bg-12-5689-2015
43. A review of carbon monitoring in wet carbon systems using remote sensing A. Campbell et al. https://doi.org/10.1088/1748-9326/ac4d4d
44. Modeling Methane Emissions from Amazon Floodplain Ecosystems C. Potter et al. https://doi.org/10.1007/s13157-014-0516-3
45. Long-term variations of the mole fraction and carbon isotope ratio of atmospheric methane observed at Ny-Ålesund, Svalbard from 1996 to 2013 S. Morimoto et al. https://doi.org/10.1080/16000889.2017.1380497
46. Carbon, Nitrogen, and Sulfur Elemental Fluxes in the Soil and Exchanges with the Atmosphere in Australian Tropical, Temperate, and Arid Wetlands C. Pasut et al. https://doi.org/10.3390/atmos12010042
47. Evolution of Atmospheric Carbon Dioxide and Methane Mole Fractions in the Yangtze River Delta, China K. Jiang et al. https://doi.org/10.3390/atmos14081295
48. Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates K. Chang et al. https://doi.org/10.1111/gcb.16755
49. How management interacts with environmental drivers to control greenhouse gas fluxes from Pacific Northwest coastal wetlands M. Schultz et al. https://doi.org/10.1007/s10533-023-01071-6
50. Mapping global non-floodplain wetlands C. Lane et al. https://doi.org/10.5194/essd-15-2927-2023
51. Model estimates of global and regional atmospheric methane emissions of wetland ecosystems S. Denisov et al. https://doi.org/10.1134/S0001433815050035
52. Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties Z. Zhang et al. https://doi.org/10.5194/bg-13-1387-2016
53. Characteristics and effect factors of CH4 emissions in single-family treatment wetlands J. Hu et al. https://doi.org/10.1016/j.ecoleng.2022.106793
54. Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets B. Zhang et al. https://doi.org/10.1016/j.atmosenv.2017.07.001
55. Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery B. DeVries et al. https://doi.org/10.3390/rs9080807
56. Understanding the glacial methane cycle P. Hopcroft et al. https://doi.org/10.1038/ncomms14383
57. Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions K. Chang et al. https://doi.org/10.1038/s41467-021-22452-1
58. Controls for multi-scale temporal variation in ecosystem methane exchange during the growing season of a permanently inundated fen F. Koebsch et al. https://doi.org/10.1016/j.agrformet.2015.02.002
59. Methane emissions from floodplains in the Amazon Basin: challenges in developing a process-based model for global applications B. Ringeval et al. https://doi.org/10.5194/bg-11-1519-2014
60. Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems X. Xu et al. https://doi.org/10.5194/bg-13-3735-2016
61. Genome-centric view of carbon processing in thawing permafrost B. Woodcroft et al. https://doi.org/10.1038/s41586-018-0338-1
62. Challenges Regionalizing Methane Emissions Using Aquatic Environments in the Amazon Basin as Examples J. Melack et al. https://doi.org/10.3389/fenvs.2022.866082
63. Ensemble estimates of global wetland methane emissions over 2000–2020 Z. Zhang et al. https://doi.org/10.5194/bg-22-305-2025
64. Simulated plant-mediated oxygen input has strong impacts on fine-scale porewater biogeochemistry and weak impacts on integrated methane fluxes in coastal wetlands Y. Zhou et al. https://doi.org/10.1007/s10533-024-01145-z
65. A global‐scale analysis of water storage dynamics of inland wetlands: Quantifying the impacts of human water use and man‐made reservoirs as well as the unavoidable and avoidable impacts of climate change P. Döll et al. https://doi.org/10.1002/eco.2175
66. Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions J. Watts et al. https://doi.org/10.1088/1748-9326/9/7/075001
67. Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling S. Miller et al. https://doi.org/10.5194/bg-13-1329-2016
68. The Global Methane Budget 2000–2017 M. Saunois et al. https://doi.org/10.5194/essd-12-1561-2020
69. Spatiotemporal Assessment of GHG Emissions and Nutrient Sequestration Linked to Agronutrient Runoff in Global Wetlands C. Pasut et al. https://doi.org/10.1029/2020GB006816
70. Implementing methane dynamics into the LPJmL6 model S. Schaphoff et al. https://doi.org/10.5194/gmd-19-7615-2026
71. Simulated Hydrological Dynamics and Coupled Iron Redox Cycling Impact Methane Production in an Arctic Soil B. Sulman et al. https://doi.org/10.1029/2021JG006662
72. The underappreciated importance of small wetlands in global methane emissions F. Li et al. https://doi.org/10.1038/s41558-026-02609-w
73. An observation-constrained assessment of the climate sensitivity and future trajectories of wetland methane emissions E. Koffi et al. https://doi.org/10.1126/sciadv.aay4444
74. Modeling strategies and data needs for representing coastal wetland vegetation in land surface models S. LaFond‐Hudson & B. Sulman https://doi.org/10.1111/nph.18760
75. Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review C. Huang et al. https://doi.org/10.1029/2018RG000598
76. Humid Subtropical Forests Constitute a Net Methane Source: A Catchment‐Scale Study L. Yu et al. https://doi.org/10.1029/2019JG005210
77. Causality guided machine learning model on wetland CH4 emissions across global wetlands K. Yuan et al. https://doi.org/10.1016/j.agrformet.2022.109115
78. Adding stable carbon isotopes improves model representation of the role of microbial communities in peatland methane cycling J. Deng et al. https://doi.org/10.1002/2016MS000817
79. Extending a land-surface model with Sphagnum moss to simulate responses of a northern temperate bog to whole ecosystem warming and elevated CO2 X. Shi et al. https://doi.org/10.5194/bg-18-467-2021
80. Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions T. Lippmann et al. https://doi.org/10.5194/gmd-16-6773-2023
81. A large‐scale methane model by incorporating the surface water transport X. Lu et al. https://doi.org/10.1002/2016JG003321
82. Disentangling methane and carbon dioxide sources and transport across the Russian Arctic from aircraft measurements C. Narbaud et al. https://doi.org/10.5194/acp-23-2293-2023
83. Greenhouse gas fluxes from Alaska's North Slope inferred from the Airborne Carbon Measurements campaign (ACME-V) J. Tadić et al. https://doi.org/10.1016/j.atmosenv.2021.118239
84. NGRIP CH4 concentration from 120 to 10 kyr before present and its relation to a δ15N temperature reconstruction from the same ice core M. Baumgartner et al. https://doi.org/10.5194/cp-10-903-2014
85. DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands B. Stocker et al. https://doi.org/10.5194/gmd-7-3089-2014
86. The global methane budget 2000–2012 M. Saunois et al. https://doi.org/10.5194/essd-8-697-2016
87. Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations P. Bergamaschi et al. https://doi.org/10.5194/acp-18-901-2018
88. Multi-source global wetland maps combining surface water imagery and groundwater constraints A. Tootchi et al. https://doi.org/10.5194/essd-11-189-2019
89. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands J. Irvin et al. https://doi.org/10.1016/j.agrformet.2021.108528
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94. Sphagnum and herbaceous net ecosystem exchanges in a Pyrenean peatland: a long-term study using the ISBA model R. Garisoain et al. https://doi.org/10.5194/bg-23-3407-2026
95. Stable atmospheric methane in the 2000s: key-role of emissions from natural wetlands I. Pison et al. https://doi.org/10.5194/acp-13-11609-2013
96. The impact of spatially varying wetland source signatures on the atmospheric variability ofδD-CH4 A. Stell et al. https://doi.org/10.1098/rsta.2020.0442
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103. The Characteristics of Soil Microbial Community Structure, Soil Microbial Respiration and their Influencing Factors of Three Vegetation Types in Alpine Wetland Ecosystem Y. Wang et al. https://doi.org/10.1007/s13157-024-01862-z
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107. A comprehensive and synthetic dataset for global, regional, and national greenhouse gas emissions by sector 1970–2018 with an extension to 2019 J. Minx et al. https://doi.org/10.5194/essd-13-5213-2021
108. Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations H. Liu et al. https://doi.org/10.5194/acp-26-1229-2026
109. Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity K. Chang et al. https://doi.org/10.5194/bg-17-5849-2020
110. Characterizing Performance of Freshwater Wetland Methane Models Across Time Scales at FLUXNET‐CH4 Sites Using Wavelet Analyses Z. Zhang et al. https://doi.org/10.1029/2022JG007259
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115. Ground subsidence effects on simulating dynamic high-latitude surface inundation under permafrost thaw using CLM5 A. Ekici et al. https://doi.org/10.5194/gmd-12-5291-2019
116. Modeling Sediment Yield in Land Surface and Earth System Models: Model Comparison, Development, and Evaluation Z. Tan et al. https://doi.org/10.1029/2017MS001270
117. Assessing the time variability of GIEMS-2 satellite-derived surface water extent over 30 years J. Bernard et al. https://doi.org/10.3389/frsen.2024.1399234
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120. Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model Q. Zhu et al. https://doi.org/10.5194/gmd-7-981-2014
121. Large-Scale Marsh Loss Reconstructed from Satellite Data in the Small Sanjiang Plain since 1965: Process, Pattern and Driving Force F. Yan https://doi.org/10.3390/s20041036
122. Plant functional types in Earth system models: past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems S. Wullschleger et al. https://doi.org/10.1093/aob/mcu077
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125. Methane Emissions from Wetlands on the Tibetan Plateau over the Past 40 Years T. Sun et al. https://doi.org/10.3390/w17162491
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130. Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions X. Yu et al. https://doi.org/10.5194/acp-21-951-2021
131. Limited response of peatland CH4 emissions to abrupt Atlantic Ocean circulation changes in glacial climates P. Hopcroft et al. https://doi.org/10.5194/cp-10-137-2014