Polarimetric hyperspectral images can provide spectral, spatial, and polarimetric information of a scene, which are unique and comprehensive for remote sensing applications such as growth monitoring of crops, analysis of water quality, and geology mapping, etc. The researches on polarimetric hyperspectral imaging mechanism and on image characteristics are of great importance for further information extraction and utilization of the images. The purposes of this paper are to analyze the mechanism of polarimetric hyperspectral imaging and to model such a process. The outcome of the paper will help designers and users of a polarimetric hyperspectral imaging system to further understand the system and take full advantages of it. In this paper, a polarimetric hyperspectral imaging model is proposed, in which the influence of skylight on polarization is considered, and subpixel model, polarized reflectance models, and the classical fast canopy reflectance model are combined to model the vegetation canopy. Then, a simulated scene that includes a woodland area with low shrubbery and a road is obtained by using the imaging model. Experiments analyze and discuss the simulation condition and parameters of the imaging models, the uniqueness, and usefulness of the integration of polarimetric and spectral information.