Abstract: Leaf area index (LAI) is one of the most important indicators that characterize canopy structure and growth of crops. LAI changes can therefore greatly contribute to the variable rate fertilization of cotton. It is of great significance to monitor LAI quickly, accurately, and non-destructively, thereby guiding crop fertilization in modern agriculture. The traditional LAI monitoring relies mainly on manual sampling with high labor intensity and time-consuming. Furthermore, the lagging data cannot meet the needs of real-time monitoring. Most studies on crop LAI have also been made using remote sensing in recent years, such as hand-held spectrometers, unmanned aerial vehicles, and satellites. Nevertheless, the near-earth surface spectrum cannot be used to continuously and rapidly monitor at the spatial scale, due to the limited shooting range and the weight of the instrument. Satellite images are mostly used for the plant LAI monitoring at forest or large regional scale, particularly on the resolution of 10-60m. Alternatively, an Unmanned Aerial Vehicle (UAV) has the potential to fast capture high resolution images repeatedly, suitable for accurate crop monitoring of small plots. Many efforts have been made to monitor the LAI of wheat, rice, corn and others using spectral images under UAVs. Since spectral technology can monitor timely and dynamically, and in macro mode, the resulting LAI spectral data really determines the vegetation index. As such, the hyperspectral reflectance of plant canopy can provide much richer information of vegetation characteristics, compared with vegetation index. However, a large amount of hyperspectral data under UAVs normally presents data redundancy and high multicollinearity. Reasonable spectral transformation can also be utilized to remove the background and noise of hyperspectral data. Correspondingly, machine learning has widely been applied to crop growth monitoring for deep information in data, particularly combined with remote sensing. Great ability of learning and prediction can be achieved using the partial least squares (PLS) model (an extension of multicollinearity model), Support Vector Machine (SVM), and Random Forest (RF), in order to reduce the collinearity between variables in different ways. In this study, the UAV hyperspectral data was preprocessed using the First Derivative (FDR), the Second Derivative (SDR), Savitzky-Golay(SG) smoothing, and Multiple Scatter Correction (MSC) under the plot experiments of different varieties and nitrogen treatments. Sensitive bands were also selected using the Pearson correlation coefficient, Successive Projections Algorithm (SPA), Shuffled Frog Leaping Algorithm (SFLA), and Competitive Adaptive Reweighting (CARS). A cotton LAI monitoring model was finally constructed to calculate the reflectance of selected bands using the Partial Least Square Regression(PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The results showed that the canopy spectra of different LAI were significantly different from 760-1000 nm, where there was a significant correlation between the canopy spectrum and LAI. The sensitive response band of LAI in the cotton canopy was concentrated in the visible light (400-780 nm) and near-infrared (after 900 nm). The highest precision and stability were achieved in the RFR model under each pretreatment for LAI monitoring. Among them, the FDR-SFLA-RFR model performed the best, where the determination coefficient, Root Mean Square Error (RMSE), and relative RMSE for the modeling dataset were 0.74, 1.648 3, and 26.39%, respectively. In the verification dataset, the determination coefficient, RMSE and relative RMSE were 0.67, 1.622 0, and 25.97%, respectively. Consequently, the optimal estimation model can be rationally selected to represent the UAV spectral reflectance of the canopy using various pretreatments, band selecting, and modeling. The findings can provide the potential basis to accurately manage the variable fertilization in cotton fields.