Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2357-2024
https://doi.org/10.5194/hess-28-2357-2024
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-430', Anonymous Referee #1, 15 May 2023
    • AC1: 'Reply on RC1', Barry van Jaarsveld, 21 Jul 2023
  • RC2: 'Comment on hess-2022-430', Anonymous Referee #2, 05 Jun 2023
    • AC2: 'Reply on RC2', Barry van Jaarsveld, 21 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (09 Oct 2023) by Thom Bogaard
AR by Barry van Jaarsveld on behalf of the Authors (05 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (07 Dec 2023) by Thom Bogaard
RR by Anonymous Referee #1 (08 Dec 2023)
RR by Anonymous Referee #3 (19 Dec 2023)
ED: Publish subject to revisions (further review by editor and referees) (26 Dec 2023) by Thom Bogaard
AR by Barry van Jaarsveld on behalf of the Authors (14 Feb 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (27 Feb 2024) by Thom Bogaard
RR by Anonymous Referee #4 (30 Mar 2024)
ED: Publish subject to minor revisions (review by editor) (02 Apr 2024) by Thom Bogaard
AR by Barry van Jaarsveld on behalf of the Authors (09 Apr 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (14 Apr 2024) by Thom Bogaard
AR by Barry van Jaarsveld on behalf of the Authors (19 Apr 2024)  Author's response   Manuscript 
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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.