Abstract: Due to the geometry of the rice canopy, the reflectance spectral information obtained by conventional UAV hyperspectroscopy contains specular reflection information which is not related to the internal composition of rice, thus affecting the inversion accuracy of the nitrogen content of rice. The inversion accuracy of rice nitrogen content was improved by removing the specular reflection component from the reflectance spectra. Based on the multi-angle polarimetric spectral data of rice tillering stage and the corresponding nitrogen content data obtained from UAV polarimetric remote sensing measurements, the correlation between them was analysed by the vegetation index method, and the angle with the highest correlation between the polarimetric spectral data of the rice canopy and its corresponding nitrogen content was obtained. The polarisation spectrum vegetation index（PSVI） was constructed based on a mathematical transformation method. The inverse model of the nitrogen content of the rice canopy was developed by using a linear regression method. The results were as follows: the best observation angle of-15°（15° for backward observation） was obtained by analyzing the correlation between the polarisation spectral data and the nitrogen content of the rice canopy at different observation zenith angles; the six characteristic bands of the polarisation spectral information at this angle were extracted by the continuous projection method, specifically 500 nm, 566 nm, 663 nm, 691 nm, 736 nm and 763 nm; the mathematical transformation idea was applied to the polarization spectral vegetation index（PSVI）, consisting of 500 nm and 566 nm was constructed; the PSVI was used as the model input, and the linear regression method was used to establish the inversion model of nitrogen content in the rice canopy. The inversion results were better than the inverse models of nitrogen content constructed by difference vegetation index（DSI）, ratio vegetation index（RVI） and other common vegetation indices. In conclusion, based on the polarization spectral data of rice tillering stage acquired by UAV and using PSVI vegetation index as model input, the accuracy of inversion of nitrogen content in rice canopy can be improved.