Using the orthogonal projections methods for predicting rice (Oryza sativa L.) yield with canopy reflectance data

Abstract

Predicting crop yield in-season over large areas before harvest is an important topic in agricultural decision-making. This study compares the performance of partial least squares regression (PLSR) for predicting rice yield (Oryza sativa L.) using different signal correction methods on canopy reflectance spectral data. These signal correction methods include the standard normal variate (SNV) transformation, multiplicative scatter correction (MSC), orthogonal signal correction algorithm with leave-one-out cross-validation (OSC(CV)), and orthogonal projections to latent structures (O-PLS). Data were acquired over a wavelength range of 350–1100 nm. However, the influence of the intra-variance based on measured dates appeared in the original spectra. Using these pre-processing methods effectively reduced the influence of noise and increased the performance of the final PLSR model. Although SNV and MSC had good predictive ability, they could not clearly identify intra-variance effects. Conversely, the PLSR models with OSC and O-PLS were based on only one component, and could be interpreted in terms of crop parameters. Moreover, the Y-orthogonal component of O-PLS clearly identified intra-variance based on measured dates and provided superior modelling ability. The results of this study show that the O-PLS method is a useful tool for correction and interpretation when constructing a PLSR model for predicting rice yield in-season using canopy reflectance data.