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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jan 2021
Research article |
| 18 Jan 2021
| 18 Jan 2021
Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland
Aurelio Guevara-Escobar
Facultad de Ciencias Naturales, Universidad Autónoma de
Querétaro, Av. de las Ciencias s/n Juriquilla, CP. 76230, Querétaro,
Querétaro, Mexico
Enrique González-Sosa
Facultad de Ingeniería, Universidad Autónoma de
Querétaro, Cerro de las Campanas s/n Las Campanas, CP. 76010
Querétaro, Querétaro, Mexico
Mónica Cervantes-Jiménez
CORRESPONDING AUTHOR
Facultad de Ciencias Naturales, Universidad Autónoma de
Querétaro, Av. de las Ciencias s/n Juriquilla, CP. 76230, Querétaro,
Querétaro, Mexico
Humberto Suzán-Azpiri
Facultad de Ciencias Naturales, Universidad Autónoma de
Querétaro, Av. de las Ciencias s/n Juriquilla, CP. 76230, Querétaro,
Querétaro, Mexico
Mónica Elisa Queijeiro-Bolaños
Facultad de Ciencias Naturales, Universidad Autónoma de
Querétaro, Av. de las Ciencias s/n Juriquilla, CP. 76230, Querétaro,
Querétaro, Mexico
Israel Carrillo-Ángeles
Facultad de Ciencias Naturales, Universidad Autónoma de
Querétaro, Av. de las Ciencias s/n Juriquilla, CP. 76230, Querétaro,
Querétaro, Mexico
Víctor Hugo Cambrón-Sandoval
Facultad de Ciencias Naturales, Universidad Autónoma de
Querétaro, Av. de las Ciencias s/n Juriquilla, CP. 76230, Querétaro,
Querétaro, Mexico
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- Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress N. Orlowski et al. 10.1016/j.scitotenv.2023.163510
- Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods D. Garcia-Rodriguez et al. 10.1016/j.ecoinf.2024.102638
- Gap Filling Method and Estimation of Net Ecosystem CO2 Exchange in Alpine Wetland of Qinghai–Tibet Plateau X. Wang et al. 10.3390/su15054652
- Monitoring Energy Balance, Turbulent Flux Partitioning, Evapotranspiration and Biophysical Parameters of Nopalea cochenillifera (Cactaceae) in the Brazilian Semi-Arid Environment A. Jardim et al. 10.3390/plants12132562
- Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. 10.5194/bg-21-2447-2024
- Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador C. Urgilés et al. 10.1007/s00484-024-02832-0
- Sink or carbon source? how the Opuntia cactus agroecosystem interacts in the use of carbon, nutrients and radiation in the Brazilian semi-arid region A. Jardim et al. 10.1016/j.jhydrol.2023.130121
- A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach C. Zhang et al. 10.1016/j.jhydrol.2021.127047
- Partitioning of water vapor and CO fluxes and underlying water use efficiency evaluation in a Brazilian seasonally dry tropical forest (Caatinga) using the Fluxpart model C. Borges et al. 10.1016/j.jsames.2024.104963
- Understanding interactive processes: a review of CO2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments A. da Rosa Ferraz Jardim et al. 10.1007/s10661-022-10339-7
- Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops T. Zenone et al. 10.1186/s13717-022-00400-1
11 citations as recorded by crossref.
- On the use of machine learning methods to improve the estimation of gross primary productivity of maize field with drip irrigation H. Guo et al. 10.1016/j.ecolmodel.2022.110250
- Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress N. Orlowski et al. 10.1016/j.scitotenv.2023.163510
- Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods D. Garcia-Rodriguez et al. 10.1016/j.ecoinf.2024.102638
- Gap Filling Method and Estimation of Net Ecosystem CO2 Exchange in Alpine Wetland of Qinghai–Tibet Plateau X. Wang et al. 10.3390/su15054652
- Monitoring Energy Balance, Turbulent Flux Partitioning, Evapotranspiration and Biophysical Parameters of Nopalea cochenillifera (Cactaceae) in the Brazilian Semi-Arid Environment A. Jardim et al. 10.3390/plants12132562
- Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. 10.5194/bg-21-2447-2024
- Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador C. Urgilés et al. 10.1007/s00484-024-02832-0
- Sink or carbon source? how the Opuntia cactus agroecosystem interacts in the use of carbon, nutrients and radiation in the Brazilian semi-arid region A. Jardim et al. 10.1016/j.jhydrol.2023.130121
- A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach C. Zhang et al. 10.1016/j.jhydrol.2021.127047
- Partitioning of water vapor and CO fluxes and underlying water use efficiency evaluation in a Brazilian seasonally dry tropical forest (Caatinga) using the Fluxpart model C. Borges et al. 10.1016/j.jsames.2024.104963
- Understanding interactive processes: a review of CO2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments A. da Rosa Ferraz Jardim et al. 10.1007/s10661-022-10339-7
Latest update: 30 Dec 2024
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.
All vegetation types can sequester carbon dioxide. We compared ground measurements (eddy...
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