Accurate, fast and non-destructive estimation of leaf area index (LAI) is of great significance for the production and management of winter wheat. Multi-spectral images were obtained by using the Prime ALTUM camera at the joining stage, booting stage, heading stage and filling stage of winter wheat, and the LAI was measured by using the LAI-2200C plant canopy analyzer. Totally twenty-five vegetation indices were selected based on the Pearson correlation analysis. And eight texture features were extracted: contrast (CON), entropy (ENT), variance (VAR), mean (MEA), homogeneity (HOM), dissimilarity (DIS), the second moment (SEM) and correlation (COR), and three color features: mean (M), variance (V) and skewness (S) were extracted as well. Then the multiple stepwise regression (MSR), support vector regression (SVR) and Gaussian process regression (GPR) models were used for winter wheat LAI inversion. The results showed that compared with single type variable-based models, models with combined texture and color features produced greater estimation accuracy;among the three types of models, GPR model outperformed the other two models in estimating winter wheat LAI;among all models, the GPR model with texture-color features and vegetation indices obtained the best estimation accuracy, with coefficient of determination (R2)of 0.94, root mean square error (RMSE) of 0.17m2/m2, mean absolute error (MAE) of 0.13m2/m2, and normal root mean square error (NRMSE) of 4.06%. The extraction of texture and color features can solve the oversaturation issue of vegetation indices under high-density canopy conditions, and more information can be derived for more accurate estimation of winter wheat LAI, which provided theoretical basis for winter wheat growth monitoring, production and management.