The novel method for LAI inversion using Lidar and hyperspectral data - Zuowei Huang; Feng Liu; Guangwei Hu (Functional Materials, 2017)
For inversion of Leaf area index (LAI) in large scale, it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively. In order to improve the estimation precision of leaf area index, an analyzing method based on Lidar and hyperspectral data was proposed. Through the processing of Lidar (Light Identification Detection and Ranging) and hyperspectral data, the LAI estimation model was established based on statistic analysis method in the study area. The results showed that the Lidar and hyperspectral data joint inversion model which considers the optical remote sensing of biophysical parameters can provide good estimates of LAI inversion, shows high accuracy (R2=0.8948, RMSE=0.2120),which reveals the great potential to enhance the accuracy of LAI estimation by using Lidar and hyperspectral data.