Robust Modelling of Soil Diffuse Reflectance Spectra by “Bagging-Partial Least Squares Regression”

Abstract

Visible (vis), near infrared (NIR) and mid infrared (mid-IR) diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) are increasingly being used in the agricultural and environmental sciences as an efficient complement to conventional laboratory analysis. The DRS techniques are rapid, relatively cheap and more efficient for obtaining data than conventional analysis, especially when a large number of samples and analyses are required. A single spectrum may be used to predict various physical, chemical and biological soil properties. The robustness of PLSR models and their predictions may be improved by combining the implementation of PLSR with bootstrap aggregation or “bagging”. Bagging aims to reduce the variance of predictions by aggregating a number of models obtained in the course of re-sampling. The aim of this work was to test the implementation of bagging with PLSR (bagging-PLSR) using vis-NIR and mid-IR soil diffuse reflectance spectra to predict soil organic carbon (OC). Bagging-PLSR was shown to: (i) be more robust than PLSR alone, (ii) be less prone to over fitting and improve prediction accuracy and (iii) provide a measure of the uncertainty of the models and their predictions.