Comparison of Parametric and Non-Parametric Methods for Chlorophyll Estimation based on High-Resolution UAV Imagery

The present study provides a systematic comparison of parametric and non-parametric retrieval methods using high-resolution data provided by the unmanned aerial vehicle (UAV). We used turmeric crop reflectance data to evaluate the vegetation index (VI)-based parametric methods and compared them with linear and nonlinear non-parametric methods to build a rigorous LCC estimation model. The study demonstrates that the best-performing VI was the normalized green red difference index (GNRDI), with R² = 0.68, RMSE = 0.13 and high processing speed of 0.08 s. With regard to non-parametric methods, almost all methods outperformed their parametric counterparts. Particularly, methods such as random forest (RF) and kernel ridge regression (KRR) showed the best performance characterized by R² > 0.72 and RMSE ≤ 0.12 mg/g of fresh leaf weight. These nonparametric methods possessed the benefit of total spectral information utilization and enabled robust, non-linear relationship between the predictor and target variables, but computational complexity is a major drawback.