Articles | Volume 21, issue 10
https://doi.org/10.5194/bg-21-2447-2024
© Author(s) 2024. 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-21-2447-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 May 2024
Research article |
| 24 May 2024
| 24 May 2024
Using automated machine learning for the upscaling of gross primary productivity
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark
Yanghui Kang
CORRESPONDING AUTHOR
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Guy Schurgers
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark
Trevor Keenan
CORRESPONDING AUTHOR
Department of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, CA 94720, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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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.
Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a...
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