Estimation of Leaf Area Index of Winter Wheat Based on Hyperspectral Data of Unmanned Aerial Vehicles

Abstract

Rapid and accurate estimation of the winter wheat leaf area index (LAI) is important for evaluating its growth and estimating yield. In this paper, Optimal Index (OI) was used to screen the best combination of hyperspectral bands in the flag stage and flowering period of wheat, and the LAI estimation model was constructed by Partial Least Square (PLS). The main results are as follows: The LAI estimation model based on the 614-774-794nm band combination is the best model for winter wheat flag stage (R2=0.485, RMSE=1.192, $R_{\text{V}}^2 = 0.682$, RMSE V =1.210); The LAI estimation model constructed by the 454-754-834nm band combination is the best model for winter wheat flowering (R2=0.702, RMSE=0.665, $R_{\text{V}}^2 = 0.810$, RMSE V =0.468). The results show that it is feasible to use the optimal band combination as an independent variable to estimate the leaf area index of winter wheat, which can be used as a new method to monitor the growth of wheat.