Abstract: Leaf Area Index (LAI) is one of the most crucial influencing parameters on crop photosynthesis during growth status. There is a high demand for the timely and accurate monitoring of maize LAI in the high crop productivity. The purpose of this research was to integrate the Unmanned Aerial Vehicle (UAV) spectral, thermal, and morphological parameters of crop canopy, in order to improve the accuracy of maize LAI estimation at various growth stages. Moreover, an attempt was also made to create the LAI inversion maps for summer maize. The optimal estimation model was achieved to facilitate precise water and nitrogen management. Firstly, the images of maize canopies were collected at multiple growth stages under different water and nitrogen treatments using multispectral and thermal infrared sensors carried by UAVs. The parameters of maize growth (LAI and crop height) were simultaneously measured in field experiments in 2022-2023. Secondly, the LAI estimation models were developed using canopy spectral, thermal infrared data, canopy morphological parameters, and their combinations. Partial Least Squares Regression (PLS), Backpropagation Neural Network (BP), and Random Forest (RF) were also selected using various machine learning algorithms. Finally, the LAI inversion maps at the in-situ scale were created using the optimal RF estimation model. It was found that the maize LAI responded significantly to the water and water-nitrogen treatments, suitable for crop growth monitoring. Among them, the LAI values were ranged from 0.61 to 4.92 (0.89 to 5.94) under the N6 (N6W2) treatment; The LAI values were ranged from 0.49 to 4.37 (0.65 to 4.08) under the N1 (N1W0) treatment. Vegetation index (VIs), normalized relative temperature (NRCT), and morphological parameters (CM) of the maize canopy showed stable correlations with the LAI. However, there were some limitations on a single source of information, in order to monitor the LAI of maize at multiple growth stages. The LAI estimation model with the spectral data shared the R² between 0.36 and 0.61, and RMSE between 0.09 and 0.57. The LAI estimation models with the thermal infrared data shared the R² between 0.25 and 0.48 and RMSE between 0.11 and 0.62. The LAI estimation models with the canopy morphology (plant height or plant height+canopy coverage) shared the R² between 0.31-0.58 and 0.50-0.67 and RMSE between 0.06-0.54 and 0.08-0.51. In contrast, the multiple sources of data (including canopy spectra, thermal infrared data, and canopy morphological parameters) were integrated to significantly improve the accuracy of LAI estimates, especially at the later stages of maize growth. The underestimation was avoided from a single source of information. Compared with the NRCT+CM and VIs+CM, the R² was improved by 15.19 % to 19.01 % and 3.87 % to 12.57 %, respectively, and RMSE was reduced by 24.93 % to 28.13 % and 9.51 % to 19.94 %, respectively. The RF model showed the highest estimation accuracy with R² = 0.814-0.867, RMSE = 0.065-0.276, and MAE = 0.048-0.207 among the three machine learning models. The inverse maps of in situ LAI with the RF model accurately reflected the water and nitrogen status of the crop. The ranges of LAI values for the different growth stages under the N1 treatment were 0.36-4.97 under the N1 treatment; The range of LAI values under the N6 treatment was 0.54-6.27. The range of LAI at different growth stages under the W0 treatment was 0.31-4.73, and the range of LAI at different growth stages under the W3 treatment was 0.39-5.84. The finding can provide a feasible technological pathway to monitor the growth status of the crop using the unmanned platform. The water and nitrogen management were optimized for the high application potential. This finding can provide a feasible technical pathway for the UAV-based monitoring of crop growth.