Assessing Sampling Design and Voxel Size in Estimating Wheat Green Area Index With Measured and Simulated TLS Data

Green Area Index (GAI) serves as an important link in the analysis of the relationship between solar energy radiation and crop grain yield. Voxel-based approach from terrestrial laser scanning (TLS) data has been widely used to measure the canopy structure due to its ability to characterize plant material within a 3D grid. However, few studies have explored how TLS sampling design and voxel size affect the accuracy of crop GAI estimation. In this study, we assessed these effects on wheat GAI estimation based on measured and simulated TLS data. We simulated different TLS sampling designs, including varying numbers of scanning sites (1, 4, 8, 12, 16) and scanning strategies (Strategy 1: peripheral distribution; Strategy 2: uniform distribution). Furthermore, a LiDAR algorithm for GAI estimation based on voxel size optimization, which we call the k-neighborhood voxel approach (KNV), was developed. The results demonstrated that TLS sampling design 2-12 (12-scan sites uniform distribution) was the most effect approach for comprehensively characterizing canopy structure across the wheat growing seasons. Uniform distribution (Strategy 2) resulted in a greater number of laser returns from lower part of the canopy, and enhanced the homogeneity of the distribution of laser points. In addition, the optimal voxel method could greatly mitigate the impacts of changes in TLS sampling design and improve the accuracy of TLS technology for estimating wheat GAI (RMSE = 0.57, RRMSE = 18.26%). This study provides valuable guidance for effective acquisition and accurate estimation of crop attributes in crop breeding and plant phenotyping.