Biogeosciences
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Articles | Volume 21, issue 2
Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-335-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-335-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 2
Research article
 | Highlight paper
 | 
19 Jan 2024
Research article | Highlight paper |  | 19 Jan 2024

High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra

Anna-Maria Virkkala, Pekka Niittynen, Julia Kemppinen, Maija E. Marushchak, Carolina Voigt, Geert Hensgens, Johanna Kerttula, Konsta Happonen, Vilna Tyystjärvi, Christina Biasi, Jenni Hultman, Janne Rinne, and Miska Luoto

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Latest update: 07 May 2024
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Arctic greenhouse gas fluxes are key for climate feedback but the Arctic greenhouse gas balance is poorly constrained due to a limited understanding of the spatial variation in these fluxes. This study combines extensive chamber-based flux measurements and remote sensing data to develop a machine-learning model to predict greenhouse gas fluxes across a tundra landscape in Finland. The analysis revealed that the system was a net greenhouse gas sink and showed widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale.
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Short summary
Arctic greenhouse gas (GHG) fluxes of CO2, CH4, and N2O are important for climate feedbacks. We combined extensive in situ measurements and remote sensing data to develop machine-learning models to predict GHG fluxes at a 2 m resolution across a tundra landscape. The analysis revealed that the system was a net GHG sink and showed widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale.
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