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 connection of remotely sensed data with key parameters in measuring and modeling net primary production (NPP). Synthetic aperture radars (SARs) are discussed in terms of their ability to measure more directly certain parameters related to NPP. The purpose of this paper is to introduce SAR-based methodologies and results for (1) deriving parameters which may be considered input datasets 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 biomass (and carbon fraction), and belowground coarse root biomass (and carbon fraction). An allometric ANPP model is applied to demonstrate the applicability of these SAR-derived datasets to NPP modeling. Results are regional quantifications and mapped distributions of forest height, above and belowground tree biomass (and carbon fraction), aboveground ANPP, and the relationship of forest stage to production.