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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Cited articles

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Anderson-Teixeira, K. J., Delong, J. P., Fox, A. M., Brese, D. A., and Litvak, M. E.: Differential responses of production and respiration to temperature and moisture drive the carbon balance across a climatic gradient in New Mexico, Glob. Change Biol., 17, 410–424, https://doi.org/10.1111/j.1365-2486.2010.02269.x, 2011. 
Baldocchi, D.: Measuring fluxes of trace gases and energy between ecosystems and the atmosphere–the state and future of the eddy covariance method, Glob. Change Biol., 20, 3600–3609, https://doi.org/10.1111/gcb.12649, 2014. 
Barron-Gafford, G. A., Sanchez-Cañete, E. P., Minor, R. L., Hendryx, S. M., Lee, E., Sutter, L. F., Tran, N., Parra, E., Colella, T., Murphy, P. C., Hamerlynck, E. P., Kumar, P., and Scott, R. L.: Impacts of hydraulic redistribution on grass–tree competition vs facilitation in a semi-arid savanna, New Phytol., 215, 1451–1461, https://doi.org/10.1111/nph.14693, 2017. 
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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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