Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-3121-2022
https://doi.org/10.5194/gmd-15-3121-2022
Development and technical paper
 | 
18 Apr 2022
Development and technical paper |  | 18 Apr 2022

Predicting global terrestrial biomes with the LeNet convolutional neural network

Hisashi Sato and Takeshi Ise

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-271', Juan Antonio Añel, 14 Aug 2021
    • AC1: 'Reply on CEC1', Hisashi Sato, 15 Aug 2021
  • RC1: 'Comment on gmd-2021-271', Anonymous Referee #1, 20 Sep 2021
  • RC2: 'Comment on gmd-2021-271', Anonymous Referee #2, 01 Dec 2021
  • RC3: 'Comment on gmd-2021-271', Anonymous Referee #3, 17 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Hisashi Sato on behalf of the Authors (01 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (03 Feb 2022) by Tomomichi Kato
RR by Anonymous Referee #2 (09 Feb 2022)
RR by Tobias Gerken (14 Feb 2022)
ED: Publish subject to minor revisions (review by editor) (15 Feb 2022) by Tomomichi Kato
AR by Hisashi Sato on behalf of the Authors (22 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (09 Mar 2022) by Tomomichi Kato
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Short summary
Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.