A global moderate resolution dataset of gross primary production of vegetation for 2000–2016

@article{Zhang2017AGM,
  title={A global moderate resolution dataset of gross primary production of vegetation for 2000–2016},
  author={Yao Zhang and Xiangming Xiao and Xiaocui Wu and Sha Zhou and Geli Zhang and Yuanwei Qin and Jinwei Dong},
  journal={Scientific Data},
  year={2017},
  volume={4}
}
Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000–2016. This GPP… 

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Large spatial‐scale effects of climate extremes on gross primary production (GPP), the largest terrestrial carbon flux, are highly uncertain even as these extremes increase in frequency and extent.

Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data

Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by
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