83 citations as recorded by crossref.

1. Methane dynamics in vegetated habitats in inland waters: quantification, regulation, and global significance P. Bodmer et al. 10.3389/frwa.2023.1332968
2. Spectral Indices of Vegetation Condition and Soil Water Content Reflect Controls on CH4 and CO2 Exchange in Sphagnum‐Dominated Northern Peatlands C. Tucker et al. 10.1029/2021JG006486
3. Quantifying Drivers of Methane Hydrobiogeochemistry in a Tidal River Floodplain System Z. Hou et al. 10.3390/w16010171
4. Towards a comprehensive and consistent global aquatic land cover characterization framework addressing multiple user needs P. Xu et al. 10.1016/j.rse.2020.112034
5. High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra A. Virkkala et al. 10.5194/bg-21-335-2024
6. Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) Z. Zhang et al. 10.5194/essd-13-2001-2021
7. Lessons learned from more than a decade of greenhouse gas flux measurements at boreal forests in eastern Siberia and interior Alaska T. Hiyama et al. 10.1016/j.polar.2020.100607
8. Bottom-up evaluation of the regional methane budget of northern lands from 1980 to 2015 A. Ito 10.1016/j.polar.2020.100558
9. Machine learning models inaccurately predict current and future high-latitude C balances I. Shirley et al. 10.1088/1748-9326/acacb2
10. The Boreal–Arctic Wetland and Lake Dataset (BAWLD) D. Olefeldt et al. 10.5194/essd-13-5127-2021
11. Unraveling the depth-dependent causal dynamics of methanogenesis and methanotrophy in a high-latitude fen peatland S. Yang et al. 10.1088/1748-9326/adaf44
12. Cold‐Season Methane Fluxes Simulated by GCP‐CH4 Models A. Ito et al. 10.1029/2023GL103037
13. Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 10.1088/1748-9326/adad02
14. Spatiotemporal variation in soil methane uptake in a cool-temperate immature deciduous forest R. Hu et al. 10.1016/j.soilbio.2023.109094
15. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands J. Irvin et al. 10.1016/j.agrformet.2021.108528
16. A strong mitigation scenario maintains climate neutrality of northern peatlands C. Qiu et al. 10.1016/j.oneear.2021.12.008
17. Estimation of Canada's methane emissions: inverse modelling analysis using the Environment and Climate Change Canada (ECCC) measurement network M. Ishizawa et al. 10.5194/acp-24-10013-2024
18. Assessing modelled methane emissions over northern wetlands by the JULES-HIMMELI model Y. Gao et al. 10.1016/j.scitotenv.2025.179526
19. Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux E. Vainio et al. 10.5194/bg-18-2003-2021
20. Methane emissions during different freezing-thawing periods from a fen on the Qinghai-Tibetan Plateau: Four years of measurements H. Chen et al. 10.1016/j.agrformet.2020.108279
21. Arctic soil methane sink increases with drier conditions and higher ecosystem respiration C. Voigt et al. 10.1038/s41558-023-01785-3
22. Air temperature and precipitation constraining the modelled wetland methane emissions in a boreal region in northern Europe T. Aalto et al. 10.5194/bg-22-323-2025
23. Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms X. Cui et al. 10.1016/j.scitotenv.2021.145130
24. Evaluation of forest carbon uptake in South Korea using the national flux tower network, remote sensing, and data-driven technology S. Cho et al. 10.1016/j.agrformet.2021.108653
25. Modeled Microbial Dynamics Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions S. Chadburn et al. 10.1029/2020GB006678
26. Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions X. Yu et al. 10.5194/acp-21-951-2021
27. Estimating 2010–2015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations S. Baray et al. 10.5194/acp-21-18101-2021
28. Studies of atmospheric climate forcers in the Arctic during the ArCS II project M. Koike et al. 10.1016/j.polar.2025.101216
29. Unraveling Spatially Diverse and Interactive Regulatory Mechanisms of Wetland Methane Fluxes to Improve Emission Estimation H. Guo et al. 10.1021/acs.est.4c06057
30. Methane emissions from rice cultivation in West Africa and compensation options from nature reserve forests S. Guug et al. 10.1088/1748-9326/adc28c
31. A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland C. Zhang et al. 10.1029/2020JG006002
32. BAWLD-CH4: a comprehensive dataset of methane fluxes from boreal and arctic ecosystems M. Kuhn et al. 10.5194/essd-13-5151-2021
33. Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity K. Yuan et al. 10.1038/s41558-024-01933-3
34. WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. 10.5194/essd-17-2507-2025
35. Utilizing Earth Observations of Soil Freeze/Thaw Data and Atmospheric Concentrations to Estimate Cold Season Methane Emissions in the Northern High Latitudes M. Tenkanen et al. 10.3390/rs13245059
36. Howland Forest, ME, USA: Multi-Gas Flux (CO2, CH4, N2O) Social Cost Product Underscores Limited Carbon Proxies B. Marino et al. 10.3390/land10040436
37. Direct measurement forest carbon protocol: a commercial system-of-systems to incentivize forest restoration and management B. Marino et al. 10.7717/peerj.8891
38. Partitioning methane flux by the eddy covariance method in a cool temperate bog based on a Bayesian framework M. UEYAMA et al. 10.1016/j.agrformet.2022.108852
39. Mires and Peatlands: Carbon, Greenhouse Gases, and Climate Change A. Sirin 10.1134/S2079086422080096
40. Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation H. Shi et al. 10.5194/bg-19-3739-2022
41. Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0) J. Bernard et al. 10.5194/gmd-18-863-2025
42. Vulnerability of Arctic-Boreal methane emissions to climate change F. Parmentier et al. 10.3389/fenvs.2024.1460155
43. Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations R. Janardanan et al. 10.3390/rs12030375
44. A review on models, products and techniques for evapotranspiration measurement, estimation, and validation M. Bariş & M. Tombul 10.1002/tqem.22250
45. Towards quantifying subsurface methane emissions from energy wells with integrity failure J. Soares et al. 10.1016/j.apr.2021.101223
46. Permafrost carbon emissions in a changing Arctic K. Miner et al. 10.1038/s43017-021-00230-3
47. Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison G. McNicol et al. 10.1029/2023AV000956
48. Global Methane Budget 2000–2020 M. Saunois et al. 10.5194/essd-17-1873-2025
49. Spatio-temporal variations of the atmospheric greenhouse gases and their sources and sinks in the Arctic region S. Morimoto et al. 10.1016/j.polar.2020.100553
50. Causality guided machine learning model on wetland CH4 emissions across global wetlands K. Yuan et al. 10.1016/j.agrformet.2022.109115
51. Ecosystem-atmosphere exchange of methane in global upland and wetland ecosystems J. Wu et al. 10.1016/j.agrformet.2024.110325
52. Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements M. Pallandt et al. 10.5194/bg-19-559-2022
53. Carbon uptake in Eurasian boreal forests dominates the high‐latitude net ecosystem carbon budget J. Watts et al. 10.1111/gcb.16553
54. Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales S. Knox et al. 10.1111/gcb.15661
55. A pragmatic protocol for characterising errors in atmospheric inversions of methane emissions over Europe B. Szénási et al. 10.1080/16000889.2021.1914989
56. Challenges and opportunities when moving food production and consumption toward sustainable diets in the Nordics: a scoping review for Nordic Nutrition Recommendations 2023 H. Meltzer et al. 10.29219/fnr.v68.10489
57. Temperate mire fluctuations from carbon sink to carbon source following changes in water table K. Fortuniak et al. 10.1016/j.scitotenv.2020.144071
58. Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half J. Liu et al. 10.1111/gcb.15247
59. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties A. Virkkala et al. 10.1111/gcb.15659
60. The Kulbäcksliden Research Infrastructure: a unique setting for northern peatland studies K. Noumonvi et al. 10.3389/feart.2023.1194749
61. The net ecosystem carbon balance (NECB) at catchment scales in the Arctic E. López-Blanco et al. 10.3389/fenvs.2025.1544586
62. Environmental controls on methane fluxes in a cool temperate bog M. Ueyama et al. 10.1016/j.agrformet.2019.107852
63. Paddy rice methane emissions across Monsoon Asia Z. Ouyang et al. 10.1016/j.rse.2022.113335
64. Geomorphological patterns of remotely sensed methane hot spots in the Mackenzie Delta, Canada L. Baskaran et al. 10.1088/1748-9326/ac41fb
65. Overview: Recent advances in the understanding of the northern Eurasian environments and of the urban air quality in China – a Pan-Eurasian Experiment (PEEX) programme perspective H. Lappalainen et al. 10.5194/acp-22-4413-2022
66. Airborne quantification of net methane and carbon dioxide fluxes from European Arctic wetlands in Summer 2019 P. Barker et al. 10.1098/rsta.2021.0192
67. Characterizing Methane Emission Hotspots From Thawing Permafrost C. Elder et al. 10.1029/2020GB006922
68. Remote sensing northern lake methane ebullition M. Engram et al. 10.1038/s41558-020-0762-8
69. Gaps in network infrastructure limit our understanding of biogenic methane emissions for the United States S. Malone et al. 10.5194/bg-19-2507-2022
70. WETMETH 1.0: a new wetland methane model for implementation in Earth system models C. Nzotungicimpaye et al. 10.5194/gmd-14-6215-2021
71. The Global Methane Budget 2000–2017 M. Saunois et al. 10.5194/essd-12-1561-2020
72. Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling A. Erkkilä et al. 10.3390/rs15245719
73. Science to Commerce: A Commercial-Scale Protocol for Carbon Trading Applied to a 28-Year Record of Forest Carbon Monitoring at the Harvard Forest N. Bautista et al. 10.3390/land10020163
74. Improved ELMv1-ECA simulations of zero-curtain periods and cold-season CH4 and CO2 emissions at Alaskan Arctic tundra sites J. Tao et al. 10.5194/tc-15-5281-2021
75. Identifying driving hydrogeomorphic factors of coastal wetland downgrading using random forest classification models K. He et al. 10.1016/j.scitotenv.2023.164995
76. Temporal Variations in Methane Emissions from a Restored Mangrove Ecosystem in Southern China P. Tian et al. 10.3390/f15091487
77. A practical metric for estimating the current climate forcing of natural mires J. Rinne et al. 10.1088/1748-9326/add607
78. Tundra fire increases the likelihood of methane hotspot formation in the Yukon–Kuskokwim Delta, Alaska, USA E. Yoseph et al. 10.1088/1748-9326/acf50b
79. Assessing methane emissions for northern peatlands in ORCHIDEE-PEAT revision 7020 E. Salmon et al. 10.5194/gmd-15-2813-2022
80. The Importance of Spatial Resolution in the Modeling of Methane Emissions from Natural Wetlands Y. Albuhaisi et al. 10.3390/rs15112840
81. Climate Sensitivity of Peatland Methane Emissions Mediated by Seasonal Hydrologic Dynamics X. Feng et al. 10.1029/2020GL088875
82. Terpene emissions from boreal wetlands can initiate stronger atmospheric new particle formation than boreal forests H. Junninen et al. 10.1038/s43247-022-00406-9
83. Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations O. Peltola et al. 10.5194/essd-11-1263-2019