Leaf area index (LAI) is one of the key indicators in the structure and function of vegetation canopy, in order to estimate the biomass and crop growth. This study aims to improve the accuracy and generalization of the LAI inversion model for the winter wheat using unmanned aerial vehicle (UAV) remote sensing. An inversion model was established using semi-empirical and semi-mechanistic approaches. An UAV with a multispectral camera was utilized to obtain the measured data of winter wheat growth with different nitrogen treatments and replanting. PROSAIL radiative transfer model was used to generate the simulated data with mechanistic information. Five LAI inversion hybrid datasets were established using different combinations of measured and simulated data. Various machine learning methods were used to construct a high-precision LAI inversion model using empirical and mechanistic information. Seven kinds of vegetation indices related to NIR bands were screened to extract the winter wheat spectral features, in order to reduce the reflectance of NIR bands. The correlation coefficient matrix between the vegetation indices and the LAI of the mixed dataset was calculated to further explore the degree of influence of different spectral features on the LAI of winter wheat. The LAI inversion models of winter wheat were formed using Bayesian ridge regression, linear regression, elasticity network, and support vector regression model. The feasibility of LAI inversion was also evaluated using semi-empirical and semi-mechanistic data. The ability of the improved model was finally determined to assess the winter wheat growth for different nitrogen levels and replanting. The results showed that: 1) There was a strong correlation between the screened vegetation indices associated with NIR bands and winter wheat LAI. Normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference red edge index (NDRE), ratio vegetation index (SR), red edge chlorophyll vegetation index (RECI), and soil adjusted vegetation index (SAVI) were positively correlated with the LAI, whereas, the structurally insensitive pigment vegetation index (SIPI) was negatively correlated with the LAI. 2) The radiative transfer model was represented for the winter wheat LAI subjected to the propagation of solar rays. The strong robustness and generalization were achieved to mix with the measured data. The support vector regression (SVR) model achieved better LAI prediction performance under various data combinations, compared with the rest. In the training set of the four training-test combinations C1, C2, C3 and C4, R² is 0.86, 0.87, 0.88, 0.91, RMSE is 0.47, 0.45, 0.45, 0.41; in the test set, R² is 0.85, 0.19, 0.89, 0.87, RMSE is 0.45, 1.31, 0.49, 0.50. 3) A support vector machine model was used to generate the LAI inversion maps for the test area. The winter wheat growth was evaluated under four nitrogen levels and two replanting models. The results showed that 180 kg/hm² fertilization was more effective than 135 kg/hm² one, but 225 kg/hm² fertilization was similar to 180 kg/hm² one. An optimal application of nitrogen treatment can be expected to improve the LAI value of winter wheat. Among them, the LAI values under wheat-bean replanting were generally higher than those of wheat-yue replanting. This finding can provide an effective way for the inversion of winter wheat LAI in the efficient assessment of winter wheat growt