Biogeosciences
Biogeosciences
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Articles | Volume 21, issue 19
Articles | Volume 21, issue 19
https://doi.org/10.5194/bg-21-4285-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-4285-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 19
Research article
 | 
02 Oct 2024
Research article |  | 02 Oct 2024

A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method

Xin Chen, Tiexi Chen, Xiaodong Li, Yuanfang Chai, Shengjie Zhou, Renjie Guo, and Jie Dai

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Latest update: 10 Oct 2024
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Short summary
We provide an ensemble-model-based GPP dataset (ERF_GPP) that explains 85.1 % of the monthly variation in GPP across 170 sites, which is higher than other GPP estimate models. In addition, ERF_GPP improves the phenomenon of “high-value underestimation and low-value overestimation” in GPP estimation to some extent. Overall, ERF_GPP provides a more reliable estimate of global GPP and will facilitate further development of carbon cycle research.
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