Abstract: Based on the MODIS surface reflectance data, five vegetation indices, including norma-lized difference vegetation index (NDVI), simple ratio index (SR), Gitelson green index (GI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) were constructed as remote sensing variables, coupled with the seven original spectral reflectance bands of MODIS. Stepwise regression and correlation analysis were used to select the variables, and the stepwise regression and Back Propagation (BP) neural network models were constructed based on the measured LAI to retrieve the LAI time series data of Phyllostachys praecox (Lei bamboo) forest during the period from January 2014 to March 2017. The retrieval results were compared with MOD15A2 LAI products during the same period. The results showed that SR was the single variable selected for the stepwise regression model. The correlations of LAI with bands b₁, b₂, b₃, b₇ and five vegetation indices were significant, which could be used as input variables of BP neural network model. There was a significant correlation between the LAI estimated from BP neural network and measured LAI, with the R² of 0.71, RMSE of 0.34, and RMSE_r of 13.6%. R² was increased by 10.9%, RMSE decreased by 5.6%, and RMSE_r decreased by 12.3% compared with LAI estimated from stepwise regression method. R² was increased by 54.5%, RMSE decreased by 79.3%, and RMSE_r decreased by 79.1% compared with MODIS LAI. The LAI of Lei bamboo forest could be accurately retrieved using BP neural network method based on MODIS reflectance time series data, which would be a feasible method for rapid monitoring of LAI in Lei bamboo forest.