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The UHD185 imaging spectrometer and ASD spectroradiometer were used to acquire imaging and nonimaging hyperspectral data during three wheat growth stages, including flagging stage, flowering stage and filling stage. The corresponding ground leaf area index (LAI) data were also collected. Firstly, the ASD and the UHD185 spectrometer data were compared and their precision was evaluated. Then, the correlation analyses were conducted between LAI and seven LAI related spectral parameters, linear regression and exponential regression were used to select the optimal estimation parameters. Finally, for each growth stage, multivariate linear regression, partial least squares, random forest, artificial neural network and support vector machine were used to construct LAI estimation models for winter wheat. The experimental results showed that UHD185 hyperspectral spectrometer reflectance was highly consistent with ASD ground hyperspectral spectrometer reflectance in the rededge region. The coefficients of determination between them were 0.9959, 0.9990 and 0.9968 for flagging stage, flowering stage and filling stages, respectively. The parameters with the highest correlation with LAI were NDVI (r=0.738) for flagging stage, SR (r=0.819) for flowering stage, and NDVI×SR (r=0.835) for filling stage. LAI-MLR was the best estimation model for winter wheat. The highest accuracy for flowering stage with R2 of 0.6788, RMSE of 0.69 and NRMSE of 19.79% for calibration, and with R2 of 0.8462, RMSE of 0.47 and NRMSE of 16.04% for validation.