Biogeosciences
Biogeosciences
BG
Articles | Volume 20, issue 4
Articles | Volume 20, issue 4
https://doi.org/10.5194/bg-20-897-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-897-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 4
Research article
 | 
02 Mar 2023
Research article |  | 02 Mar 2023

Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange

Matti Kämäräinen, Juha-Pekka Tuovinen, Markku Kulmala, Ivan Mammarella, Juha Aalto, Henriikka Vekuri, Annalea Lohila, and Anna Lintunen

Viewed

Total article views: 2,878 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,041 742 95 2,878 101 122
  • HTML: 2,041
  • PDF: 742
  • XML: 95
  • Total: 2,878
  • BibTeX: 101
  • EndNote: 122
Views and downloads (calculated since 11 May 2022)
Cumulative views and downloads (calculated since 11 May 2022)

Viewed (geographical distribution)

Total article views: 2,878 (including HTML, PDF, and XML) Thereof 2,809 with geography defined and 69 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Nov 2025
Download
Short summary
In this study, we introduce a new method for modeling the exchange of carbon between the atmosphere and a study site located in a boreal forest in southern Finland. Our method yields more accurate results than previous approaches in this context. Accurately estimating carbon exchange is crucial for gaining a better understanding of the role of forests in regulating atmospheric carbon and addressing climate change.
Read more
Share
Altmetrics
Final-revised paper
Preprint