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

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 valida...

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Bibliographic Details
Published inScientific data Vol. 4; no. 1; p. 170165
Main Authors Zhang, Yao, Xiao, Xiangming, Wu, Xiaocui, Zhou, Sha, Zhang, Geli, Qin, Yuanwei, Dong, Jinwei
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 24.10.2017
Nature Publishing Group
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Summary: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 dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies. Design Type(s) data integration objective • time series design • modeling and simulation objective Measurement Type(s) ecosystem-wide photosynthesis Technology Type(s) computational modeling technique Factor Type(s) Sample Characteristic(s) Earth (Planet) • vegetation layer • temperature of environmental material • land • radiation • vegetated area Machine-accessible metadata file describing the reported data (ISA-Tab format)
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Y.Z. and X.X. designed the study, Y.Z. and X.W. generate the data, G.Z. contributed to the LSWI algorithm. Y.Z., X.X. and S.Z. analyzed the data, Y.Z. wrote the paper. All authors reviewed and edited the manuscript.
ISSN:2052-4463
2052-4463
DOI:10.1038/sdata.2017.165