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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Published in | Scientific data Vol. 4; no. 1; p. 170165 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
24.10.2017
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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 |