Constructing 3D SPAD distribution using hyperspectral LiDAR point cloud by PROSPECT model inversion

Abstract

ABSTRACT Accurately estimating leaf chlorophyll concentrations in a three-dimensional (3D) manner is crucial for precisely monitoring crop growth and development. To reduce destructive detection and achieve real-time estimation, researchers often utilize crop leaves’ SPAD (Soil and Plant Analyzer Development) to represent relative chlorophyll content. However, SPAD measurement is time-consuming and laborious work. Many inversion methods have been proposed to estimate SPAD based on one-dimensional reflectance or two-dimensional spectral images, which rely on external light sources and also cannot obtain 3D distribution. Hyperspectral LiDAR (HSL), as an active remote sensing technology, can acquire target spatial and spectral data simultaneously, making 3D SPAD inversion and reconstruction possible. This research aims to invert SPAD values with HSL spectral information aided by the PROSPECT model and reconstruct 3D SPAD distribution with HSL spatial information. Firstly, we constructed new ratio chlorophyll indices (RCI) based on the multi-feature analysis method with the PROSPECT model and HSL spectral data. Next, we employed RCIs to modify the Look-Up Table (LUT) cost function (SPAD-LUT) of the PROSPECT model, which was validated with the ANGERS dataset. Then, we conducted SPAD inversion with HSL spectral information from vegetable crop samples and integrated the inverted SPAD values into their corresponding 3D coordinates, achieving 3D SPAD distribution reconstruction. The predicted values of the model inversion with the HSL dataset are consistent with the measured SPAD (R2 = 0.421, RMSE = 10.615). The results indicate that our method can invert sample SPAD values and reconstruct their distribution with HSL point cloud despite their complex spatial structure.