In order to rapidly learn the rice canopy phosphorus content in the field, an imaging spectrometer (Cubert S185) mounted on a UAV was used to acquire the hyperspectral images of rice canopy in an experimental field and the leaves of each plot were sampled for leaf phosphorus content (LPC) measurement in the laboratory. The spectral features of the LPC in the UAV hyperspectral images were analyzed. The characteristic wavelengths of LPC were selected using the successive projections algorithm (SPA). Three spectral indices which were normalized difference spectral index (NDSI), ratio spectral index (RSI) and difference spectral index (DSI), were calculated by combing each two bands. The correlation analysis was performed between LPC and each spectral index in order to screen the most related spectral indices. LPC estimation models were built based on the spectral reflectance of the characteristic wavelength and the spectral indices using multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN). The rice LPC distribution maps of each growth stage were made by computing the hyperspectral images pixel-by-pixel using the best LPC estimation model. The results showed that the LPC had significant negative correlations with the spectral reflectance within the range of 462~718 nm and the highest correlation coefficient reached -0.902. By using SPA, 670 nm, 706 nm, 722 nm and 846 nm were chosen as the characteristic wavelengths of LPC. The LPC estimation model which was built based on the four characteristic wavelengths using PLSR method achieved the highest accuracy and the validation R2 value reached 0.925 and the RMSE was 0.027%. Among all the spectral indices, NDSI(R498,R606), RSI(R498, R606), and DSI(R498,R586) had the highest correlation with LPC and the correlation coefficients were 0.913, 0.915 and 0.938, respectively. The validation R2 values of the ANN models based on the three spectral indices was 0.885 and the RMSE was 0.029%. The predicted LPC values derived from the LPC distribution map of each growth stage were consistent with the measured values. Therefore, the UAV-based hyperspectral remote sensing technology could provide a rapid and nondestructive method to monitor the phosphorus status of rice leaves on the field scale.