Articles | Volume 19, issue 16
Biogeosciences, 19, 3739–3756, 2022
https://doi.org/10.5194/bg-19-3739-2022
Biogeosciences, 19, 3739–3756, 2022
https://doi.org/10.5194/bg-19-3739-2022
Research article
16 Aug 2022
Research article | 16 Aug 2022

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

Haiyang Shi et al.

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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 bg-2022-46', Anonymous Referee #1, 06 Apr 2022
  • RC2: 'Comment on bg-2022-46', Anonymous Referee #2, 09 Apr 2022
    • AC2: 'Reply on RC2', Haiyang Shi, 07 May 2022
  • RC3: 'Comment on bg-2022-46', Anonymous Referee #3, 09 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (12 May 2022) by Paul Stoy
AR by Haiyang Shi on behalf of the Authors (07 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (17 Jun 2022) by Paul Stoy
RR by Anonymous Referee #3 (22 Jun 2022)
ED: Publish subject to minor revisions (review by editor) (24 Jun 2022) by Paul Stoy
AR by Haiyang Shi on behalf of the Authors (26 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (16 Jul 2022) by Paul Stoy
AR by Haiyang Shi on behalf of the Authors (24 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (25 Jul 2022) by Paul Stoy
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Short summary
A number of studies have been conducted by using machine learning approaches to simulate carbon fluxes. We performed a meta-analysis of these net ecosystem exchange (NEE) simulations. Random forests and support vector machines performed better than other algorithms. Models with larger timescales had a lower accuracy. For different plant functional types (PFTs), there were significant differences in the predictors used and their effects on model accuracy.
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