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.
The files are named by the following rules. "GPP.VPM.v20.YYYY.monthly.CMG_0.05.nc" or "GPP.VPM.v20.YYYY.8-day.CMG_0.05.nc" are for the monthly and 8-day temporal resolutions, respectively. "YYYY" represents the year. The units for the monthly data is g C m^-2 month^-1, and is g C m^-2 day^-1 for the 8-day data.
张尧. (2021). Global Gross Primary Production (GPP) data by Vegetation Photosynthesis Model. 时空三极环境大数据平台,
DOI:
10.6084/m9.figshare.c.3789814.v1.
[Zhang, Y. (2021). . A Big Earth Data Platform for Three Poles,
DOI:
10.6084/m9.figshare.c.3789814.v1.
]
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1. Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., & Dong, J. (2015) A global moderate resolution dataset of gross primary production of vegetation for 2000–2016, Scientific Data, 4:170165( 查看 | Bibtex格式)
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