Hyperspectral field spectroscopy and SENTINEL-2 Multispectral data for minerals with high pollution potential content estimation and mapping

Belgacem Dkhala; Nouha Mezned; Cécile Gomez; Saadi Abdeljaouad

doi:10.1016/j.scitotenv.2020.140160

Hyperspectral field spectroscopy and SENTINEL-2 Multispectral data for minerals with high pollution potential content estimation and mapping

Sci Total Environ. 2020 Oct 20:740:140160. doi: 10.1016/j.scitotenv.2020.140160. Epub 2020 Jun 11.

Authors

Belgacem Dkhala¹, Nouha Mezned², Cécile Gomez³, Saadi Abdeljaouad⁴

Affiliations

¹ Laboratory of Mineral Resources and Environment, Department of Geology, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia. Electronic address: dkhalakacem@gmail.com.
² Laboratory of Mineral Resources and Environment, Department of Geology, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia; Higher Institute of Preparatory Studies in Biology and Geology of Soukra, Institution of Agricultural Research and Higher Education, University of Carthage, Tunis, Tunisia.
³ LISAH, University of Montpellier, INRAE, IRD, Montpellier SupAgro, 34060 Montpellier, France; Indo-French Cell for Water Sciences, IRD, Indian Institute of Science, Bangalore 560012, India.
⁴ Laboratory of Mineral Resources and Environment, Department of Geology, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia.

PMID: 32927579
DOI: 10.1016/j.scitotenv.2020.140160

Abstract

Mining in Tunisia generates a large amount of tailings charged with toxic minerals. As these tailings have a wide spread distribution, it is important to characterize and estimate their impact on soil contamination. This study examines the potential of field hyperspectral spectroscopy and SENTINEL-2 Multispectral data in estimating and mapping seven minerals content, including three toxic minerals (fluorite, barite and sphalerite), within soils around Hammam Zriba mine in Northen Tunisia. 69 soil and dike surface samples were collected, field Visible, Near InfraRed (VNIR) and Short-Wave InfraRed (SWIR) reflectance spectra were measured on these surfaces. The X-ray diffraction (XRD) method was used to identify the types of mineral and their associated contents on each collected soil samples. The mineral contents were predicted using the partial least squares regression (PLSR) method using i) field VNIR-SWIR spectra at raw spectral resolution, ii) field VNIR-SWIR spectra aggregated to the SENTINEL-2 spectral resolution and then iii) SENTINEL-2 spectra. This study shows 1) an accurate prediction of four of the seven minerals using field VNIR-SWIR spectroscopy, 2) a slight decrease of performances due to spectral resolution degradation (SENTINEL-2 simulated spectra) and 3) a significant decrease of performances due to spatial resolution degradation, except for fluorite. This work paves the way for large-scale mapping of minerals with high pollution potential using SENTINEL-2 data. In addition, the high frequency of SENTINEL-2 data may be used to monitor the spatial distribution of some minerals with high pollution potential in soils.

Keywords: Field hyperspectral spectroscopy; Mapping; Mine tailings; Minerals with high pollution potential; PLSR; SENTINEL-2 data.