In this paper a hybrid technique of image classification, modeling, and inversion algorithm is introduced in order to extract vegetation biomass from polarimetric SAR data. The development of the hybrid technique has evolved from SAR data analysis and the mere fact that the SAR signal, being correlated with vegetation type and moisture content, saturates as the biomass increases. The technique consists of the following steps: (1) classification of the image to land cover map, (2) classification of the SAR image into scattering mechanisms, (3) formulation of a multilayer forest backscatter model, (4) piecewise inversion of the model to estimate the vegetation water content for various components of the forest canopy, and (5) estimation of the forest biomass by combining the inversion and classification results. This technique has been tested over the boreal forest in Canada by using the multipolarization, multifrequency AIRSAR data. The results indicate that the use of the hybrid technique enhances the estimation of the forest biomass. The general form of the technique and its various components can be regarded as a basic approach to resolve the problem of biomass estimation with SAR data. The modification of this technique into more sophisticated forms can improve the biomass estimation in particular over dense tropical forests.