[Objective] In order to further tap the potential of unmanned aerial vehicle (UAV) carried LiDAR to monitor crop growth and to explore effect of merging UAV LiDAR with multispectral data in inversing leaf area index (LAI) in winter wheat, this study was carried out.[Method] In this study, with the aid of UAV LiDAR scanners and visible-near infrared multispectral cameras, UAV LiDAR point cloud and multispectral data of the winter wheat at the booting stage in experiment zone were collected. From the data, four LiDAR point cloud structure parameters, i.e., three-dimensional volumetric parameters (BIOVP), mean plant height (Hmean), 75 percentile plant height (H75) and laser penetration index (LPI), and six vegetation indices, i.e., NDVI, SAVI, MCARI, TVI, NDRE and RVI were extracted. Then correlation analysis was performed of these parameters for screening suitable modeling parameters. With the aid of the multiple linear regression (MLR) and the partial least squares regression (PLSR), a LAI inversion model was constructed through merging the LiDAR point cloud structure parameters with vegetation indices as input parameters of the model. In applying the MLR method, the two vegetation indices, NDVI and SAVI, that are the most closely correlated with the field-observed LAI and the two point cloud structure parameters, H75 and BIOVP, that are the most closely correlated with the field-observed LAI, were used as input parameters of the model. While in adopting the PLSR method, the number of principal components in modeling was determined in the light of the result of the cross-validation. Before modeling, the experimental dataset had been randomly divided into a modeling set (n=32) and a validation set (n=16) at a ratio of 7:3 in all treatments. A LAI inversion model was built up based on the modeling dataset and then the validation dataset was used to evaluate effect of the model. Meanwhile, in order to determine whether the inversion with the LiDAR point cloud data merged with the multispectral data was better than that based on multispectral data alone, LAI inversion models were constructed using the same modeling method with vegetation indices as input parameters of the model only.[Result] The evaluation of the model using the coefficient of determination (R²) and the root mean square error (RMSE) shows that the inversion model using the LiDAR point cloud data merged with the multispectral data well reflect the LAI in winter wheat, with R² of the modeling set being all > 0.900 and RMSE being < 0.400 and R² of the validation set being all > 0.800, and RMSE being < 0.500. In addition, no matter whether using MLR or PLSR, the models with the LiDAR point cloud data and vegetation indices (MLR:R²=0.901, RMSE=0.480; PLSR:R²=0.909, RMSE=0.445 (n=16)) are all superior to the models using vegetation indices only (MLR:R²=0.897, RMSE=0.492; PLSR:R²=0.892, RMSE=0.486 (n=16)).[Conclusion] In conclusion, although the LAI data in this research were too scattered, leading to insignificant difference between models in comparison, it could still be seen that the addition of UAV LiDAR data could make up for the defect of using the multispectral data alone that insufficient information could be extracted along the vertical direction of the crop, and improve accuracy of the inversion of LAI in winter wheat. Therefore, the model with UAV LiDAR data merged with multispectral data is a superior means for inversion of LAI in winter wheat and even other crops smaller in plant type.