Biogeosciences
Biogeosciences
BG
Articles | Volume 22, issue 15
Articles | Volume 22, issue 15
https://doi.org/10.5194/bg-22-3965-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-3965-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 15
Research article
 | 
14 Aug 2025
Research article |  | 14 Aug 2025

On the added value of sequential deep learning for the upscaling of evapotranspiration

Basil Kraft, Jacob A. Nelson, Sophia Walther, Fabian Gans, Ulrich Weber, Gregory Duveiller, Markus Reichstein, Weijie Zhang, Marc Rußwurm, Devis Tuia, Marco Körner, Zayd Hamdi, and Martin Jung

Viewed

Total article views: 996 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
500 160 336 996 40 55
  • HTML: 500
  • PDF: 160
  • XML: 336
  • Total: 996
  • BibTeX: 40
  • EndNote: 55
Views and downloads (calculated since 10 Oct 2024)
Cumulative views and downloads (calculated since 10 Oct 2024)

Viewed (geographical distribution)

Total article views: 996 (including HTML, PDF, and XML) Thereof 986 with geography defined and 10 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Aug 2025
Download
Short summary
This study evaluates machine learning approaches for upscaling evapotranspiration from the site to the global scale. Sequential models capture temporal dynamics better, especially with precipitation data, but all models show biases in data-scarce regions. Improved upscaling requires richer training data, informed covariate selection, and physical constraints to enhance robustness and reduce extrapolation errors.
Read more
Share
Altmetrics
Final-revised paper
Preprint