Leaf Area Index（LAI） is an important indicator to reflect the growth state of crops， which is usually estimated by vegetation index. Traditional inversion models are mostly based on multivariate regression models， while the potential of multivariate regression models based on bivariates in LAI inversion has not been fully explored. By extracting the spectral features and texture features of satellite images， the correlation between each remote sensing feature and winter wheat LAI was analyzed based on Pearson correlation coefficient. Using Simple Regression model （SR）， Multiple Linear Regression model （MLR） and Random Forest Regression model （RFR）， the relationship between remote sensing characteristics and LAI of winter wheat was studied. The inversion accuracy of each inversion model was determined by the accuracy index （determination coefficient R²， root mean square error RMSE， relative root mean square error rRMSE）. Based on the above evaluation indicators， the optimal inversion model was proposed. The results showed： （1） All vegetation indexes and some texture indexes have achieved good inversion results in LAI inversion （R²>0.6）. Among them， the Universal Normalized Vegetation Index performed the best among all vegetation indices （R²=0.754，RMSE=0.606，rRMSE=12.99%）. Except for the mean feature inversion accuracy of some bands that is comparable to vegetation index， the accuracy of most texture feature inversion for the winter wheat LAI is poor； （2） The bivariate multivariate linear regression model with the highest LAI inversion accuracy for winter wheat was obtained through bivariate combination （R²=0.780，RMSE=0.573，rRMSE=12.29%）； （3）In the case of multiple input variables （at least 3 feature variables）， RFR performed better than MLR. Compared to texture features， the inversion performance of texture indices was better. The research results can provide a new approach and method for monitoring large-scale crop LAI based on satellite imagery in the future.