Estimating olive leaf traits from spectrometer data using PROSPECT inversion methods with variable Refractive Index

Understanding leaf traits is crucial for the early detection of plant stress. Remote sensing offers the potential to estimate these traits non-destructively. This research explores the inversion of the PROSPECT Radiative Transfer Model for retrieving structural and biochemical properties of olive tree leaves from reflectance spectra within the visible-Near Infra-Red domain. To achieve this, we investigate the impact of fixed and variable Refractive Index (RI) on the estimation accuracy using two inversion methods: Look-Up Table (LUT) and Particle Swarm Optimization (PSO). Field-measured reflectance spectra using a spectrometer are used to evaluate the performance of the inversion methods. The Mean Squared Error (MSE) metric is used for this assessment. The results of the study demonstrate that the use of a variable RI significantly improves the accuracy of leaf trait estimation compared to a fixed RI approach for both inversion methods, which produce similar precisions.