Plot experiments of the winter rapeseed （Brassica napus L.） with different nitrogenous levels under direct seeding treatment were conducted in 2014-2015.The canopy spectral reflectance,soil background,LAI of each plot were measured at different stages.Correlation analysis between the canopy spectral reflectance and LAI was used to calculate eleven vegetation indices and twelve spectral parameters based on spectral position and area for optimizing five kinds of linear and nonlinear (logarithm,parabola,power and exponential) quantitative remote sensing inversion models to estimate LAI at the different and whole growth stages.The results showed that the quadratic polynomial inversion models perfectly estimated LAI of winter rapeseed using hyperspectral techniques.The spectral red edge parameters estimated accurately LAI at seedling stage.The predicted models based on Dr,NBR,Dr produced better estimation for LAI at six-leaf stage，eight-leaf stage and tenleaf stage,respectively.R2 was 0.81,0.79 and 0.92(P<0.01),respectively.RMSEP (root mean square error of predicted models) was 0.39,0.60 and 0.47,respectively. RPD (residual predictive deviation) was 1.62,2.30 and 2.36,respectively.The predicted models based on SDb and RDVI produced better estimation for LAI at full-bloom stage and pod stage with R2 of 0.87 and 0.74(P<0.01),RMSEP of 0.34 and 0.57,and RPD of 2.57 and 1.36.The unified validation of models(R2 0.75,RMSEP>0.65,RPD<1.4) showed that there was low prediction precision with the unified spectral variables or vegetation indices monitoring LAI at the whole stages of growth.The prediction accuracy of monitoring model based on the appropriate spectral variables and vegetation indices to estimate LAI at different stages of the winter rapeseed growth was high.