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 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…

• NS
  National Science Foundation

  Awards: IIA-1301789, 1301789
• NA
  National Aeronautics and Space Administration

  Awards: 80LARC17C0001, 2013-69002
• UD
  U.S. Department of Agriculture

  Awards: 2013-69002-23146, 2016-68002-24967, 2013-69002
• YU
  Yale University
• NI
  National Institute of Food and Agriculture

  Awards: 2016-68002-24967, 2013-69002-23146, 2013-69002
• LR
  Langley Research Center
• OO
  Office of Experimental Program to Stimulate Competitive Research

  Award: IIA-1301789