Abstract: This study focused on retrieval of net photosynthetic rate (P_n) of moso bamboo forest based on analysis of wavelet transform on hyperspectral reflectance data of moso bamboo forest leaf. The result showed that the accuracy of P_n retrieved by the ideal high frequency wavelet vegetation index (VI) was higher than that retrieved by low frequency wavelet VI and spectral VI. Normalized difference vegetation index of wavelet (NDVI_w), simple ratio vegetation index of wavelet (SR_w) and difference vegetation index of wavelet (D_w) constructed by the first layer of high frequency coefficient through wavelet decomposition had the highest relationship with P_n, with the R² of 0.7 and RMSE of 0.33; low frequency wavelet VI had no advantage compared with spectral VI. Significant correlation existed between P_n estimated by multivariate linear model constructed by the ideal wavelet VI and the measured P_n, with the R² of 0.77 and RMSE of 0.29, and the accuracy was significantly higher than that of using the spectral VI. Compared with the fact that sensitive spectral bands of the retrieval through spectral VI were limited in the range of visible light, the wavelength of sensitive bands of wavelet VI ranged more widely from visible to infrared bands. The results illustrated that spectrum of wavelet transform could reflect the P_n of moso bamboo more in detail, and the ove-rall accuracy was significantly improved than that using the original spectral data, which provided a new alternative method for retrieval of P_n of moso bamboo forest using hyper spectral remotely sensed data.