Articles | Volume 21, issue 19
https://doi.org/10.5194/bg-21-4285-2024
https://doi.org/10.5194/bg-21-4285-2024
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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Cited articles

Ai, Z., Wang, Q., Yang, Y., Manevski, K., Yi, S., and Zhao, X.: Variation of gross primary production, evapotranspiration and water use efficiency for global croplands, Agr. Forest Meteorol., 287, 107935, https://doi.org/10.1016/j.agrformet.2020.107935, 2020. 
Ambika, A. K. and Mishra, V.: Substantial decline in atmospheric aridity due to irrigation in India, Environ. Res. Lett., 15, 107935, https://doi.org/10.1088/1748-9326/abc8bc, 2020. 
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, Rev. Geophys., 53, 785–818, https://doi.org/10.1002/2015rg000483, 2015. 
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv., 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017. 
Badgley, G., Anderegg, L. D., Berry, J. A., and Field, C. B.: Terrestrial gross primary production: Using NIRV to scale from site to globe, Glob. Change Biol., 25, 3731–3740, https://doi.org/10.1111/gcb.14729, 2019. 
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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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