Radiation calibration is the core issue of quantitative remote sensing. Polarization spectrum detection can characterize fine structure. In order to improve the accuracy of remote sensing under outdoor lighting conditions, the multi-angle polarized hyperspectral images of the jujube canopy were obtained, and the quality distribution characteristics of the jujube canopy were analyzed by extracting multi-dimensional information features such as angle, polarization and spectrum from hyperspectral images. Secondly, the normalized vegetation index NDVI, With the polarization parameter such as the dolp (degree of linear polarization) and Orient to obtain the polarization parameter images of jujube. To get the other targets with obvious features and clear texture in details. Finally, by performing multi-band fitting and related processing on the hyperspectral characterizing the water content and other quality information of the jujube, a polarized hyperspectral quantitative remote sensing model was established to obtain grayscale images that characterize the spatial distribution of the water content and other qualities. This provides an important reference for the development of digital intelligent agriculture.