Our previous work on the remote-sensing based data assimilation framework has been extended to regional and global scales. It employs a subgrid‐scale representation of plant functional types (PFTs) and elevation classes to generate a globally applicable phenological parameter set. We are able to predict (or hindcast) a 50 year long (1960–2009) daily 1° × 1° global phenology climate data record with a mean FPAR and LAI prediction error of 0.065 (−) and 0.34 (m^2 m^−2).
Mean annual leaf area index (LAI) from our satellite-model reanalysis project
The data set as well as the underlying data assimilation and prediction code with all parameters are available publicly as open source:
http://phenoanalysis.sourceforge.net
R. Stockli, T. Rutishauser, I. Baker, M. Liniger, and A. S. Denning. A global reanalysis of vegetation phenology. J. Geophys. Res. – Biogeo- sciences, 116(G03020), 2011. doi: 10.1029/2010JG001545.