Articles | Volume 18, issue 2
https://doi.org/10.5194/bg-18-367-2021
https://doi.org/10.5194/bg-18-367-2021
Research article
 | 
18 Jan 2021
Research article |  | 18 Jan 2021

Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland

Aurelio Guevara-Escobar, Enrique González-Sosa, Mónica Cervantes-Jiménez, Humberto Suzán-Azpiri, Mónica Elisa Queijeiro-Bolaños, Israel Carrillo-Ángeles, and Víctor Hugo Cambrón-Sandoval

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (28 Apr 2020) by Paul Stoy
AR by Anna Wenzel on behalf of the Authors (16 Jul 2020)  Author's response
ED: Referee Nomination & Report Request started (16 Jul 2020) by Paul Stoy
RR by Anonymous Referee #2 (22 Jul 2020)
RR by Anonymous Referee #3 (18 Aug 2020)
ED: Reconsider after major revisions (18 Aug 2020) by Paul Stoy
AR by Svenja Lange on behalf of the Authors (29 Sep 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (29 Sep 2020) by Paul Stoy
RR by Anonymous Referee #3 (12 Oct 2020)
ED: Publish subject to technical corrections (15 Nov 2020) by Paul Stoy
AR by Mónica Cervantes-jimenez on behalf of the Authors (22 Nov 2020)  Author's response    Manuscript
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Short summary
All vegetation types can sequester carbon dioxide. We compared ground measurements (eddy covariance) with remotely sensed data (Moderate Resolution Imaging Spectroradiometer, MODIS) and machine learning ensembles of primary production in a semiarid scrub in Mexico. The annual carbon sink for this vegetation type was −283.5 g C m−2 y−1; MODIS underestimated the primary productivity, and the machine learning modeling was better locally.
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