Articles | Volume 21, issue 10
https://doi.org/10.5194/bg-21-2447-2024
https://doi.org/10.5194/bg-21-2447-2024
Research article
 | 
24 May 2024
Research article |  | 24 May 2024

Using automated machine learning for the upscaling of gross primary productivity

Max Gaber, Yanghui Kang, Guy Schurgers, and Trevor Keenan

Related authors

CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization
Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, and Trevor Keenan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-337,https://doi.org/10.5194/essd-2023-337, 2023
Revised manuscript under review for ESSD
Short summary

Related subject area

Biogeochemistry: Air - Land Exchange
Anticorrelation of net uptake of atmospheric CO2 by the world ocean and terrestrial biosphere in current carbon cycle models
Stephen E. Schwartz
Biogeosciences, 21, 5045–5057, https://doi.org/10.5194/bg-21-5045-2024,https://doi.org/10.5194/bg-21-5045-2024, 2024
Short summary
Impact of meteorological conditions on the biogenic volatile organic compound (BVOC) emission rate from eastern Mediterranean vegetation under drought
Qian Li, Gil Lerner, Einat Bar, Efraim Lewinsohn, and Eran Tas
Biogeosciences, 21, 4133–4147, https://doi.org/10.5194/bg-21-4133-2024,https://doi.org/10.5194/bg-21-4133-2024, 2024
Short summary
Monitoring cropland daily carbon dioxide exchange at field scales with Sentinel-2 satellite imagery
Pia Gottschalk, Aram Kalhori, Zhan Li, Christian Wille, and Torsten Sachs
Biogeosciences, 21, 3593–3616, https://doi.org/10.5194/bg-21-3593-2024,https://doi.org/10.5194/bg-21-3593-2024, 2024
Short summary
Compound soil and atmospheric drought (CSAD) events and CO2 fluxes of a mixed deciduous forest: the occurrence, impact, and temporal contribution of main drivers
Liliana Scapucci, Ankit Shekhar, Sergio Aranda-Barranco, Anastasiia Bolshakova, Lukas Hörtnagl, Mana Gharun, and Nina Buchmann
Biogeosciences, 21, 3571–3592, https://doi.org/10.5194/bg-21-3571-2024,https://doi.org/10.5194/bg-21-3571-2024, 2024
Short summary
Similar freezing spectra of particles on plant canopies as in air at a high-altitude site
Annika Einbock and Franz Conen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2067,https://doi.org/10.5194/egusphere-2024-2067, 2024
Short summary

Cited articles

AmeriFlux Management Project: AmeriFlux, AmeriFlux [data set], https://ameriflux.lbl.gov/data/flux-data-products, last access: 13 October 2022. 
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C., Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Sitch, S., Viovy, N., Wiltshire, A., and Zhao, M.: Spatiotemporal patterns of terrestrial gross primary production: A review: GPP Spatiotemporal Patterns, Rev. Geophys., 53, 785–818, https://doi.org/10.1002/2015RG000483, 2015. 
Babaeian, E., Paheding, S., Siddique, N., Devabhaktuni, V. K., and Tuller, M.: Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning, Remote Sens. Environ., 260, 112434, https://doi.org/10.1016/j.rse.2021.112434, 2021. 
Balaji, A. and Allen, A.: Benchmarking Automatic Machine Learning Frameworks, ArXiv [preprint], https://doi.org/10.48550/arXiv.1808.06492, 2018. 
Download
Short summary
Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a vital role in the carbon cycle. We can measure GPP locally, but producing larger and continuous estimates is challenging. Here, we present an approach to extrapolate GPP to a global scale using satellite imagery and automated machine learning. We benchmark different models and predictor variables and achieve an estimate that can capture 75 % of the variation in GPP.
Altmetrics
Final-revised paper
Preprint