Integration of remotely sensed radar imagery in modeling and mapping of forest biomass and net primary production

New remote sensing programs provide the opportunity to optimize the connect ion of remotely sensed data with key parameters in measuring and modeling n et primary production (NPP). Synthetic aperture radars (SARs) are discussed in terms of their ability to measure more directly certain parameters rela ted to NPP. The purpose of this paper is to introduce SAR-based methodologi es and results for (1) deriving parameters which may be considered input da tasets for NPP models and (2) the subsequent application of an aboveground annual NPP (ANNP) model for these datasets. Derivations are land cover and biophysical parameters including forest height, aboveground forest tree bio mass (and carbon fraction), and belowground coarse root biomass (and carbon fraction). An allometric ANPP model is applied to demonstrate the applicab ility of these SAR-derived datasets to NPP modeling. Results are regional q uantifications and mapped distributions of forest height, above and belowgr ound tree biomass (and carbon fraction), aboveground ANPP, and the relation ship of forest stage to production. (C) 1999 Elsevier Science B.V. All righ ts reserved.