Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model
Puchen Yan, Qisheng Han, Yangming Feng, Shaozhong Kang
Abstract
Leaf area index(LAI) is an important indicator of crop growth and water status. With the continuous development of precision agriculture, estimating LAI using an unmanned aerial vehicle (UAV) remote sensing has received extensive attention due to its low cost, high throughput and accuracy. In this study, multispectral and light detection and ranging (LiDAR) sensors carried by a UAV were used to obtain multisource data of a cotton field. The method to accurately relate ground measured data with UAV data was built using empirical statistical regression models and machine learning algorithm models (RFR, SVR and ANN). In addition to the traditional spectral parameters, it is also feasible to estimate LAI using UAVs with LiDAR to obtain structural parameters. Machine learning models, especially the RFR model (R2 = 0.950, RMSE = 0.332), can estimate cotton LAI more accurately than empirical statistical regression models. Different plots and years of cotton datasets were used to test the model robustness and generality; although the accuracy of the machine learning model decreased overall, the estimation accuracy based on structural and multisources was still acceptable. However, selecting appropriate input parameters for different canopy opening and closing statuses can alleviate the degradation of accuracy, where input parameters select multisource parameters before canopy closure while structural parameters are selected after canopy closure. Finally, we propose a gap fraction model based on a LAImax threshold at various periods of cotton growth that can estimate cotton LAI with high accuracy, particularly when the calculation grid is 20 cm (R2 = 0.952, NRMSE = 12.6%). This method does not require much data modeling and has strong universality. It can be widely used in cotton LAI prediction in a variety of environments.