Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 11
Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 11
Research article
 | 
05 Jun 2024
Research article |  | 05 Jun 2024

Machine learning and global vegetation: random forests for downscaling and gap filling

Barry van Jaarsveld, Sandra M. Hauswirth, and Niko Wanders

Viewed

Total article views: 1,973 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,486 431 56 1,973 32 39
  • HTML: 1,486
  • PDF: 431
  • XML: 56
  • Total: 1,973
  • BibTeX: 32
  • EndNote: 39
Views and downloads (calculated since 08 Feb 2023)
Cumulative views and downloads (calculated since 08 Feb 2023)

Viewed (geographical distribution)

Total article views: 1,973 (including HTML, PDF, and XML) Thereof 1,955 with geography defined and 18 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Oct 2024
Download
Short summary
Drought often manifests itself in vegetation; however, obtaining high-resolution remote-sensing products that are spatially and temporally consistent is difficult. In this study, we show that machine learning (ML) can fill data gaps in existing products. We also demonstrate that ML can be used as a downscaling tool. By relying on ML for gap filling and downscaling, we can obtain a more holistic view of the impacts of drought on vegetation.
Read more