A MODIS-Based Global 1-Km Maximum Green Vegetation Fraction Dataset. Broxton, P. D.; Zeng, X.; Scheftic, W.; and Troch, P. A. 53(8):1996–2004.
A MODIS-Based Global 1-Km Maximum Green Vegetation Fraction Dataset [link]Paper  doi  abstract   bibtex   
Global land-cover data are widely used in regional and global models because land cover influences land-atmosphere exchanges of water, energy, momentum, and carbon. Many models use data of maximum green vegetation fraction (MGVF) to describe vegetation abundance. MGVF products have been created in the past using different methods, but their validation with ground sites is difficult. Furthermore, uncertainty is introduced because many products use a single year of satellite data. In this study, a global 1-km MGVF product is developed on the basis of a ” climatology” of data of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index and land-cover type, which removes biases associated with unusual greenness and inaccurate land-cover classification for individual years. MGVF shows maximum annual variability from 2001 to 2012 for intermediate values of average MGVF, and the standard deviation of MGVF normalized by its mean value decreases nearly monotonically as MGVF increases. In addition, there are substantial differences between this climatology and MGVF data from the MODIS Continuous Fields (CF) Collection 3, which is currently used in the Community Land Model. Although the CF data only use 2001 MODIS data, many of these differences cannot be explained by usage of different years of data. In particular, MGVF as based on CF data is usually higher than that based on the MODIS climatology from this paper. It is difficult to judge which product is more realistic because of a lack of ground truth, but this new MGVF product is more consistent than the CF data with the MODIS leaf area index product (which is also used to describe vegetation abundance in models).
@article{broxtonMODISbasedGlobal1km2014,
  title = {A {{MODIS}}-Based Global 1-Km Maximum Green Vegetation Fraction Dataset},
  author = {Broxton, Patrick D. and Zeng, Xubin and Scheftic, William and Troch, Peter A.},
  date = {2014-05},
  journaltitle = {Journal of Applied Meteorology and Climatology},
  volume = {53},
  pages = {1996--2004},
  issn = {1558-8432},
  doi = {10.1175/jamc-d-13-0356.1},
  url = {https://doi.org/10.1175/jamc-d-13-0356.1},
  abstract = {Global land-cover data are widely used in regional and global models because land cover influences land-atmosphere exchanges of water, energy, momentum, and carbon. Many models use data of maximum green vegetation fraction (MGVF) to describe vegetation abundance. MGVF products have been created in the past using different methods, but their validation with ground sites is difficult. Furthermore, uncertainty is introduced because many products use a single year of satellite data. In this study, a global 1-km MGVF product is developed on the basis of a ” climatology” of data of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index and land-cover type, which removes biases associated with unusual greenness and inaccurate land-cover classification for individual years. MGVF shows maximum annual variability from 2001 to 2012 for intermediate values of average MGVF, and the standard deviation of MGVF normalized by its mean value decreases nearly monotonically as MGVF increases. In addition, there are substantial differences between this climatology and MGVF data from the MODIS Continuous Fields (CF) Collection 3, which is currently used in the Community Land Model. Although the CF data only use 2001 MODIS data, many of these differences cannot be explained by usage of different years of data. In particular, MGVF as based on CF data is usually higher than that based on the MODIS climatology from this paper. It is difficult to judge which product is more realistic because of a lack of ground truth, but this new MGVF product is more consistent than the CF data with the MODIS leaf area index product (which is also used to describe vegetation abundance in models).},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13477887,bioclimatic-predictors,global-scale,limiting-factor,mapping,modis,ndvi,open-data,remote-sensing,vegetation},
  number = {8}
}
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