Abstract: The actual number of samples is much less than the dimensionality of hyperspectrum. The LAI estimation model based on the whole spectrum is unstable. This paper presented a dimensionality reduction based LAI estimation method which was the combination of two band selection methods and principal component regression (PCR) or partial least squares regression (PLSR). The first band selection method is based on the correlation coefficient between original spectral reflectance and LAI, and the second band selection method is based on characteristic of spectral curve. Five representative vegetation indices including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Renormalized Difference Vegetation Index (RDVI), Modified Soil-Adjusted Vegetation Index (MSAVI) and Triangular Vegetation Index (TVI), were chosen. Using field measured winter wheat canopy hyperspectral data and LAI in 2009 and 2010, the proposed dimensionality reduction based LAI estimation methods and vegetation index based LAI estimation methods were compared. The experimental results indicated that the overall accuracy of dimensionality reduction based LAI estimation methods were higher than that of vegetation index based LAI estimation methods, generally. The model combing the proposed first band selection method and PLSR got the highest overall estimation accuracy with R2=0.818, RMSE=0.685 in cross-validation experiments. At the same time, the models combing the proposed two band selection methods with PCR respectively also reached high overall LAI estimation accuracy.