Winter Wheat Canopy Height Estimation Based on the Fusion of LiDAR and Multispectral Data

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat canopy height can improve field management efficiency and optimize fertilization and irrigation. Changes in the growth characteristics of wheat at different growth stages affect the canopy structure, leading to changes in the quality of the LiDAR point cloud (e.g., lower density, more noise points). Multispectral data can capture these changes in the crop canopy and provide more information about the growth status of wheat. Therefore, a method is proposed that fuses LiDAR point cloud features and multispectral feature parameters to estimate the canopy height of winter wheat. Low-altitude unmanned aerial systems (UASs) equipped with LiDAR and multispectral cameras were used to collect point cloud and multispectral data from experimental winter wheat fields during three key growth stages: green-up (GUS), jointing (JS), and booting (BS). Analysis of variance, variance inflation factor, and Pearson correlation analysis were employed to extract point cloud features and multispectral feature parameters significantly correlated with the canopy height. Four wheat canopy height estimation models were constructed based on the Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, and Support Vector Regression models. The model training results showed that the OP-RF model provided the best performance across all three growth stages of wheat. The coefficient of determination values were 0.921, 0.936, and 0.842 at the GUS, JS, and BS, respectively. The root mean square error values were 0.009 m, 0.016 m, and 0.015 m. The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. At the same time, it was obtained that the estimation results of fusing point cloud features and multispectral feature parameters were better than the estimation results of a single type of feature parameters. The results meet the requirements for canopy height prediction. These results demonstrate that the fusion of point cloud features and multispectral parameters can improve the accuracy of crop canopy height monitoring. The method provides a valuable method for the remote sensing monitoring of phenotypic information of low and densely planted crops and also provides important data support for crop growth assessment and field management.