Mapping of sun-induced fluorescence (SIF) in kiwifruit canopy using a 3D radiative transfer modeling and airborne hyperspectral imaging

Sun-induced chlorophyll fluorescence (SIF) has emerged as a valuable proxy for estimating plant physiological activity. While empirical and one-dimensional (1D) radiative transfer models (RTMs) have shown reasonable success in quantifying SIF at the canopy scale using hyperspectral sensors, they face challenges in addressing complex, heterogeneous canopy structures, weak signals, and intricate sun-to-sensor geometries. In recent years, three-dimensional (3D) RTMs have made significant progress in overcoming these challenges. This study employed multiple RTMs, such as PROSPECT-PRO, FLUSPECT, and LESS, to investigate SIF in kiwifruit orchards. High-resolution hyperspectral imagery and LiDAR data were collected over the orchards, along with ground-truth measurements. A 3D kiwifruit canopy was reconstructed using functional-structural plant modeling (FSPM) based on LiDAR point cloud data. Utilizing the LESS RTM, thousands of reflectance spectra were simulated based on the given leaf and soil optical properties and the 3D canopy structure. A kernel ridge regression (KRR) algorithm was trained on these simulations in the SIF region (650–810 nm) and validated with the ground-truth measurements. This hybrid (3D RTM-KRR) model demonstrated a high correlation with the ground-truth data, outperforming empirical models (such as Fraunhofer line discrimination methods). This indicates its capability to extract SIF from coarse-resolution airborne and satellite-based hyperspectral missions (e.g., PRISMA and EnMAP). This approach offers a promising avenue for improving our understanding of plant physiological processes and their interactions with the environment at larger scales. This research provides a significant advancement for precision agriculture in orchards, proving the practical value of 3D RTM for heterogeneous canopies. • A hybrid method based on 3D radiative transfer modeling (3D-RTM) and kernel ridge regression (KRR) was found to be an effective method for retrieving SIF. • The performance of 3D-RTM-KRR was compared with Fraunhofer line discrimination methods. • An accurate kiwifruit canopy structure was generated using LiDAR point cloud data and functional-structural plant modeling (FSPM). • The correlation between SIF values and biochemical traits was explored.