Abstract: Abstract: The method of inverting leaf area index (LAI) using a single vegetation index (VI) was influenced by different degrees of saturation and each index could contain in general two bands of information. This paper proposed the method of using support vector machine regression (SVR) for leaf area index inversion, which could use more band information as input parameters in order to improve LAI inversion accuracy. Using the winter wheat's actual spectra measurement and leaf area index data in the period of erecting stage, elongation stage and filling stage, we established a NDVI-LAI and RVI-LAI model with the statistical regression method respectively, and established regression prediction model using NDVI, RVI, as well as blue, green, red and near-infrared four-band data as input parameters with the support vector machine regression (SVR) method, namely the NDVI-SVR, RVI-SVR and NRGB-SVR model. The above five models used the corresponding period environment HJ-CCD data for validation respectively. The results showed that: the RMSE of 0.98, 0.97 with the prediction accuracy value of 59.2%, 59.3% was obtained using the NDVI-LAI and RVI-LAI regression model respectively, and the RMSE of 0.71, 0.83 with the prediction accuracy value of 70.4%, 67.1% was obtained using NDVI-SVR and RVI-SVR regression model respectively. With blue (B), green (G), red (R) and near infrared (NIR) bands as input parameters of support vector machine regression and prediction, the RMSE value is 0.39, the prediction accuracy value is 81.7%. Support vector machine regression (SVR) prediction has a better fitting effect, and can input more band information to improve the leaf area index remote sensing inversion accuracy which is suitable for winter wheat's multiple birth period.