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

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

doi:https://doi.org/10.5194/bg-18-367-2021

Articles | Volume 18, issue 2

https://doi.org/10.5194/bg-18-367-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-18-367-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 2

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

Data sets

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 Suzan-Azpiri, Monica Elisa Quijeiro-Bolaños, Israel Carrillo-Angeles, and Victor Hugo Cambrón-Sandoval https://doi.org/10.5281/zenodo.3598595

Model code and software

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 Suzan-Azpiri, Monica Elisa Quijeiro-Bolaños, Israel Carrillo-Angeles, and Victor Hugo Cambrón-Sandoval https://doi.org/10.5281/zenodo.3598595

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.