72 citations as recorded by crossref.

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2. Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements M. Pallandt et al. 10.5194/bg-19-559-2022
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4. Field-scale CH<sub>4</sub> emission at a subarctic mire with heterogeneous permafrost thaw status P. Łakomiec et al. 10.5194/bg-18-5811-2021
5. Controls on carbon dioxide and methane fluxes from a low-center polygonal peatland in the Mackenzie River Delta, Northwest Territories J. Skeeter et al. 10.1139/as-2021-0034
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8. Cold season emissions dominate the Arctic tundra methane budget D. Zona et al. 10.1073/pnas.1516017113
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10. Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil R. Taki et al. 10.1139/cjss-2018-0041
11. Biophysical controls on interannual variability in ecosystem‐scale CO2 and CH4 exchange in a California rice paddy S. Knox et al. 10.1002/2015JG003247
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13. Quantifying the impact of emission outbursts and non-stationary flow on eddy-covariance CH<sub>4</sub> flux measurements using wavelet techniques M. Göckede et al. 10.5194/bg-16-3113-2019
14. Methane flux measurements in rice by static flux chamber and eddy covariance M. Reba et al. 10.1002/agg2.20119
15. Error characterization of methane fluxes and budgets derived from a long-term comparison of open- and closed-path eddy covariance systems M. Deventer et al. 10.1016/j.agrformet.2019.107638
16. Overriding control of methane flux temporal variability by water table dynamics in a Southern Hemisphere, raised bog J. Goodrich et al. 10.1002/2014JG002844
17. Assessing the carbon and climate benefit of restoring degraded agricultural peat soils to managed wetlands K. Hemes et al. 10.1016/j.agrformet.2019.01.017
18. Ethene, propene, butene and isoprene emissions from a ponderosa pine forest measured by relaxed eddy accumulation R. Rhew et al. 10.5194/acp-17-13417-2017
19. Variations and drivers of methane fluxes from a rice-wheat rotation agroecosystem in eastern China at seasonal and diurnal scales S. Dai et al. 10.1016/j.scitotenv.2019.07.012
20. Temporal trends in methane emissions from a small eutrophic reservoir: the key role of a spring burst S. Waldo et al. 10.5194/bg-18-5291-2021
21. The Fallow Period Plays an Important Role in Annual CH4 Emission in a Rice Paddy in Southern Brazil C. Maboni et al. 10.3390/su132011336
22. Greenhouse gas fluxes over managed grasslands in Central Europe L. Hörtnagl et al. 10.1111/gcb.14079
23. Technical note: Uncertainties in eddy covariance CO<sub>2</sub> fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches J. Yao et al. 10.5194/acp-21-15589-2021
24. Environmental drivers of methane fluxes from an urban temperate wetland park T. Morin et al. 10.1002/2014JG002750
25. A cool-temperate young larch plantation as a net methane source - A 4-year continuous hyperbolic relaxed eddy accumulation and chamber measurements M. Ueyama et al. 10.1016/j.atmosenv.2018.04.025
26. 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
27. Assessing diel variation of CH4 flux from rice paddies through temperature patterns C. Centeno et al. 10.1016/j.atmosenv.2017.08.007
28. Estimating the methane flux of the Dajiuhu subalpine peatland using machine learning algorithms and the maximal information coefficient technique X. Li et al. 10.1016/j.scitotenv.2024.170241
29. Carbon fluxes of an alpine peatland in Northern Italy J. Pullens et al. 10.1016/j.agrformet.2016.01.012
30. Impact of different eddy covariance sensors, site set-up, and maintenance on the annual balance of CO2 and CH4 in the harsh Arctic environment J. Goodrich et al. 10.1016/j.agrformet.2016.07.008
31. Estimating regional effects of climate change and altered land use on biosphere carbon fluxes using distributed time delay neural networks with Bayesian regularized learning A. Schmidt et al. 10.1016/j.neunet.2018.08.004
32. Impacts of Clear-Cutting of a Boreal Forest on Carbon Dioxide, Methane and Nitrous Oxide Fluxes P. Vestin et al. 10.3390/f11090961
33. Revisiting the partitioning of net ecosystem exchange of CO2 into photosynthesis and respiration with simultaneous flux measurements of 13CO2 and CO2, soil respiration and a biophysical model, CANVEG P. Oikawa et al. 10.1016/j.agrformet.2016.12.016
34. Improved gap filling approach and uncertainty estimation for eddy covariance N2O fluxes J. Goodrich et al. 10.1016/j.agrformet.2020.108280
35. Investigation of the climatological impacts of agricultural management and abandonment on a boreal bog in western Newfoundland, Canada M. Wang et al. 10.1016/j.scitotenv.2019.134632
36. How do land use practices affect methane emissions from tropical peat ecosystems? G. Wong et al. 10.1016/j.agrformet.2019.107869
37. Large methane emissions from a subarctic lake during spring thaw: Mechanisms and landscape significance M. Jammet et al. 10.1002/2015JG003137
38. Development of a tailored combination of machine learning approaches to model volumetric soil water content within a mesic forest in the Pacific Northwest A. Schmidt et al. 10.1016/j.jhydrol.2020.125044
39. FLUXNET-CH<sub>4</sub>: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands K. Delwiche et al. 10.5194/essd-13-3607-2021
40. Gap-filling carbon dioxide, water, energy, and methane fluxes in challenging ecosystems: Comparing between methods, drivers, and gap-lengths S. Zhu et al. 10.1016/j.agrformet.2023.109365
41. Quantification of the response of global terrestrial net primary production to multifactor global change P. Li et al. 10.1016/j.ecolind.2017.01.021
42. Comprehensive Evaluation of Machine Learning Techniques for Estimating the Responses of Carbon Fluxes to Climatic Forces in Different Terrestrial Ecosystems X. Dou & Y. Yang 10.3390/atmos9030083
43. Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms X. Cui et al. 10.1016/j.scitotenv.2021.145130
44. Near-zero methane emission from an abandoned boreal peatland pasture based on eddy covariance measurements M. Wang et al. 10.1371/journal.pone.0189692
45. Predicting greenhouse gas fluxes in coastal salt marshes using artificial neural networks M. Zaki & O. Abdul-Aziz 10.1007/s13157-022-01558-2
46. Six years of ecosystem-atmosphere greenhouse gas fluxes measured in a sub-boreal forest A. Richardson et al. 10.1038/s41597-019-0119-1
47. Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands P. Oikawa et al. 10.1002/2016JG003438
48. KarsTS: an R package for microclimate time series analysis M. Sáez et al. 10.1007/s12145-019-00393-0
49. Advances in the Eddy Covariance Approach to CH4 Monitoring Over Two and a Half Decades T. Morin 10.1029/2018JG004796
50. Combination of two methodologies, artificial neural network and linear interpolation, to gap-fill daily nitrous oxide flux measurements L. Bigaignon et al. 10.1016/j.agrformet.2020.108037
51. A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland C. Zhang et al. 10.1029/2020JG006002
52. Gap-Filling of Surface Fluxes Using Machine Learning Algorithms in Various Ecosystems I. Huang & C. Hsieh 10.3390/w12123415
53. The seasonal and diurnal dynamics of methane flux at a created urban wetland T. Morin et al. 10.1016/j.ecoleng.2014.02.002
54. A simple calculation algorithm to separate high-resolution CH<sub>4</sub> flux measurements into ebullition- and diffusion-derived components M. Hoffmann et al. 10.5194/amt-10-109-2017
55. Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation X. Dou & Y. Yang 10.1016/j.scitotenv.2018.01.202
56. Temporal shifts in controls over methane emissions from a boreal bog M. Wang et al. 10.1016/j.agrformet.2018.07.002
57. 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
58. Methane and carbon dioxide fluxes of a temperate mire in Central Europe K. Fortuniak et al. 10.1016/j.agrformet.2016.08.023
59. Modelling growing season carbon fluxes at a low-center polygon ecosystem in the Mackenzie River Delta J. Skeeter et al. 10.1139/as-2022-0033
60. Time-lag effects of flood stimulation on methane emissions in the Dongting Lake floodplain, China T. Wang et al. 10.1016/j.agrformet.2023.109677
61. Southern Hemisphere bog persists as a strong carbon sink during droughts J. Goodrich et al. 10.5194/bg-14-4563-2017
62. Carbon fluxes and soil carbon dynamics along a gradient of biogeomorphic succession in alpine wetlands of Tibetan Plateau H. Wang et al. 10.1016/j.fmre.2022.09.024
63. Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta N. Rößger et al. 10.1016/j.agrformet.2018.06.026
64. Methane emissions from subtropical wetlands: An evaluation of the role of data filtering on annual methane budgets C. Staudhammer et al. 10.1016/j.agrformet.2022.108972
65. Vegetation influence and environmental controls on greenhouse gas fluxes from a drained thermokarst lake in the western Canadian Arctic J. Skeeter et al. 10.5194/bg-17-4421-2020
66. Global Research Alliance N2O chamber methodology guidelines: Guidelines for gap‐filling missing measurements C. Dorich et al. 10.1002/jeq2.20138
67. Methane emissions from an alpine wetland on the Tibetan Plateau: Neglected but vital contribution of the nongrowing season W. Song et al. 10.1002/2015JG003043
68. Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half J. Liu et al. 10.1111/gcb.15247
69. Improving N2O emission estimates with the global N2O database C. Dorich et al. 10.1016/j.cosust.2020.04.006
70. Deepened snow cover alters biotic and abiotic controls on nitrogen loss during non-growing season in temperate grasslands Z. Jia et al. 10.1007/s00374-020-01514-4
71. Modeling temporal patterns of methane effluxes using multiple regression and random forest in Poyang Lake, China L. Liu et al. 10.1007/s11273-017-9558-7
72. Forest ecosystem changes from annual methane source to sink depending on late summer water balance J. Shoemaker et al. 10.1002/2013GL058691