Biogeosciences
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Articles | Volume 23, issue 2
Articles | Volume 23, issue 2
https://doi.org/10.5194/bg-23-683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 2
Research article
 | 
26 Jan 2026
Research article |  | 26 Jan 2026

Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest

Sumonta Kumar Paul, Keisuke Yuasa, Masako Dannoura, and Daniel Epron

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Latest update: 07 May 2026
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Short summary
We used a machine learning approach to upscale CH4 fluxes over time on non-waterlogged soil in a topographically complex mountain forest. Predicted CH4 fluxes varied significantly across topographic positions, with greater uptake on ridges and slopes than on the plain and foot slopes. Recent past precipitations significantly influenced seasonal CH4 uptake. Our findings highlight the role of topography and the potential of remote sensing and machine learning to map CH4 fluxes.
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