PLSR Method for the Content Prediction of High Contamination Risk Minerals Using Sentinel-2 and Spectroscopic Data: Case Study of Hammam Bent Jedidi

Abstract

Mine tailings of Hammam Bent Jedidi (F-Ba-Zn-Pb), which are part of the mining residues, may contain a large amount of valuable minerals such as by heavy metals (Pb, Zn, etc,) and minerals with high pollution potential (Ba and F). However, these tailings are continually exposed to erosion and thus destroys the normal function of soils and endangers human health. The characterization, detection, and prediction of the abundance of minerals with high pollution potential is the first step towards mitigating their impact. In this context, X-ray diffraction (XRD) method was used to characterize of mineral types and their associated contents on each collected soil samples. The partial least squares regression (PLSR) method was processed for the prediction of mineral contents using: i) VNIR-SWIR field Hyperspectral reflectance data and ii) SENTINEL-2 Multispectral data.