Hyperspectral Analysis for a Robust Assessment of Soil Properties Using Adapted PLSR Method

Abstract

Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibration steps, in order to estimate various soil properties throughout appropriate statistical models. However, one of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands. This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients (and, difficult to interpret). Moreover, in hyperspectral spectrum, the information concerning the chemical specificity is spread over several wavelengths. Therefore, it is not wise to remove this redundancy because this removal affects both relevant and irrelevant hyperspectral information. In this study, the faced challenge is to optimize the estimation of some soil properties by exploiting all the spectral richness of the hyperspectral data by providing complementary rather than redundant information. To this end, a new reliable approach based on hyperspectral data analysis and partial least squares regression is proposed.

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

“…Soil property is predictable by VNIR spectroscopy when it is correlated with chemical specificity, according to [5,6,59]. Indeed, due to the phenomenon's high variability, the interaction between matter and electromagnetic wavelengths (within the soil spectrum sample) is not directly exploitable.…”