Abstract: Leaf area index (LAI) is a key indicator for the growth of crops. The crop yield is also closely related to the LAI, particularly for the decision-making in modern agriculture. Rapid and accurate detection of the crop LAI is of great significance to field production management. Single sensors have been mostly used to monitor the winter wheat LAI in the past, such as high-definition digital cameras, multispectral and hyperspectral cameras. Fortunately, Unmanned Aerial Vehicles (UAV) remote sensing technology has been developed for crop LAI monitoring by virtue of the high timeliness and low cost at present. The multi-source image data can also be combined for parameter monitoring. In addition, previous LAI estimation is limited to either only the correlation of image features with LAI, or only the importance of image features. Therefore, this study aims to comprehensively consider the correlation of image feature with the LAI and image feature importance, and then construct the multi-source remote sensing LAI estimation models using the selection of optimal image features. Baihu Farm in Lujiang County and Shucheng County Agricultural Science Institute in Anhui Province of China were selected as the study areas, where the canopy visible and hyperspectral images of winter wheat at flowering and filling stages were collected by a UAV platform equipped with a high-definition digital camera and an imaging hyperspectrometer. Meanwhile, the ground LAI data was collected using LAI-2200C. The multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR) algorithms were selected to estimate the wheat LAI using the visible and hyperspectral image data. Firstly, the correlation between the visible and hyperspectral image features with the winter wheat LAI was analyzed as well as the importance of image features were calculated, where the optimal image features were selected. Secondly, the MLR, SVR, and RFR estimation models (single-sensor data sources) were constructed, where the input was taken as the visible vegetation index, the texture features, visible vegetation index combined with texture features, hyperspectral band, hyperspectral vegetation index, and hyperspectral band combined with vegetation index. The RFR and SVR LAI estimation models (two sensor data sources) were constructed with two image-preferred features to compare the performance of single-source and multi-source image features for the monitoring of wheat LAI. Thirdly, the spatial heterogeneity of plot soils was considered for the wheat LAI monitoring. The wheat LAI estimation model was also constructed to combine the single image features under different image sampling areas. The results showed that the best accuracy of the RFR LAI estimation model was achieved at the flowering and filling stages using two image preferred features, with the validation set R2 of 0.733 and 0.929 and RMSE of 0.193 and 0.118, respectively. By contrast, the model using the combination of single image features performed the best at the flowering and filling stages, when the sampling areas of visible images were 30% and 50%, and the sampling areas of hyperspectral images were 65%, respectively. In summary, the study can provide a valuable reference for the UAV remote sensing monitoring of crop physiological parameters.