Estimating Vertical Chlorophyll Concentrations in Maize in Different Health States Using Hyperspectral LiDAR

Abstract

The detection of vertical heterogeneity in vegetation has attracted an increasing attention as it has a great significance for precise agriculture. The hyperspectral light detection and ranging (LiDAR) (HSL) can obtain the spectral and spatial information simultaneously. However, its ability to monitor the vertical distribution of biochemical parameters in plants has not been fully explored. In this article, the applicability of empirical ratio and normalized spectral indices for HSL channels in chlorophyll (Chl) detection was investigated using three data sets: the PROSPECT-5 synthetic data set, the ANGERS public data set, and an HSL-measured data set. A linear regression model of the best performing index against measured Chl values was constructed so as to build 3-D Chl point clouds of maize. The performance of HSL in Chl detection at the upper and lower layers was also tested based on the selected spectral index. The result showed that the CIred edge index was most compatible with the HSL channels. The estimated Chl concentrations of the upper and lower layers showed the close relationships with HSL measurements ( $R^{2} = 0.73$ and 0.91, respectively). The vertical Chl profiles in maize were also presented, indicating that the HSL system has a strong ability to monitor the vertical distribution of maize Chl concentrations. This article provides a basis for the vertical detection of vegetation biochemical parameters directly from HSL measurements.