Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Zheng, Yi; Shen, Ruoque; Wang, Yawen; Li, Xiangqian; Liu, Shuguang; Liang, Shunlin; Chen, Jing M.; Ju, Weimin; Zhang, Li; Yuan, Wenping

doi:https://doi.org/10.5194/essd-12-2725-2020

Articles | Volume 12, issue 4

https://doi.org/10.5194/essd-12-2725-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-12-2725-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 4

Data description paper

|

12 Nov 2020

Data description paper |

| 12 Nov 2020

Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan

Supplement

https://doi.org/10.5194/essd-12-2725-2020-supplement

Data sets

Improved estimate of global gross primary production for reproducing its long-term variation, 1982-2017 Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan https://doi.org/10.6084/m9.figshare.8942336.v3

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Short summary

Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.