This paper presents a pragmatic method to produce global maps of vegetation parameters, which offer essential data for weather forecast and climate modeling. The crucial variables are leaf area index (LAI), fractional vegetation cover (FVC), and fraction of absorbed photosynthetically active radiation (fAPAR). The approach relies on the use of spectral and directional vegetation indices simulated by a bidirectional reflectance model and calibrated against sets of satellite data. The model belongs to the kernel-driven category, and the coefficients obtained, as the result of linear inversion, are the basis of the proposed method. The strategy presented relies upon the existence of suitable angular measurements to derive each biophysical parameter. An application is shown with the global POLDER/ADEOS-I database. Special attention is given here to the future production of LAI and fAPAR since the albedo is a product already disseminated by the POLDER production center. Terrestrial ecosystems show a high level of aggregation and, in practice, only effective LAI can be measured. Therefore a correction factor, namely the clumping index, must be applied to help resolve the scaling issue. Clumping corrections are performed biome by biome, using empirical equations where it appears that LAI assessments for boreal and tropical forests would otherwise be significantly inaccurate. However, the effect of clumping will be less on FVC and fAPAR. The relevance of the proposed method is demonstrated through a comparison of POLDER-derived LAI values with a varied set of ground LAI measurements, including their coherence with the corresponding fAPAR.