Detecting rare earth elements using EnMAP hyperspectral satellite data

a case study from Mountain Pass, California

verfasst von: Saeid Asadzadeh, Nicole Koellner, Sabine Chabrillat
Abstract: Rare earth elements (REEs) exhibit diagnostic absorption features in the visible-near infrared region, enabling their detection and identification via spectroscopic methods. Satellite-based remote sensing mapping of REEs, however, has not been attainable so far due to the necessity for high-quality hyperspectral data to resolve their narrow absorption features. This research leverages EnMAP hyperspectral satellite data to map REEs in Mountain Pass, California—a mining area known to host bastnaesite-Ce ore in sövite and beforsite carbonatites. By employing a polynomial fitting technique to characterize the diagnostic absorption features of Neodymium (Nd) at ∼740 and ∼800 nm, the surface occurrence of Nd was successfully mapped at a 30m pixel resolution. The relative abundance of Nd was represented using the continuum-removed area of the 800 nm feature. The resulting map, highlighting hundreds of anomalous pixels, was validated through laboratory spectroscopy, surface geology, and high-resolution satellite imagery. This study marks a major advancement in REE exploration, demonstrating for the first time, the possibility of directly detecting Nd in geologic environments using the EnMAP hyperspectral satellite data. This capability can offer a fast and cost-effective method for screening Earth’s surfaces for REE signature, complementing the existing exploration portfolio and facilitating the discovery of new resources.
Organisationseinheit(en): Institut für Bodenkunde
Externe Organisation(en): Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ)
Section of Remote Sensing and Geoinformatics
Typ: Artikel
Journal: Scientific reports
Band: 14
ISSN: 2045-2322
Publikationsdatum: 2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Allgemein
Elektronische Version(en): https://doi.org/10.1038/s41598-024-71395-2 (Zugang: Offen)

BibTeX

@article{2258ffcf03524d3a9a76727192cb11ba,
title = "Detecting rare earth elements using EnMAP hyperspectral satellite data: a case study from Mountain Pass, California",
abstract = "Rare earth elements (REEs) exhibit diagnostic absorption features in the visible-near infrared region, enabling their detection and identification via spectroscopic methods. Satellite-based remote sensing mapping of REEs, however, has not been attainable so far due to the necessity for high-quality hyperspectral data to resolve their narrow absorption features. This research leverages EnMAP hyperspectral satellite data to map REEs in Mountain Pass, California—a mining area known to host bastnaesite-Ce ore in s{\"o}vite and beforsite carbonatites. By employing a polynomial fitting technique to characterize the diagnostic absorption features of Neodymium (Nd) at ∼740 and ∼800 nm, the surface occurrence of Nd was successfully mapped at a 30m pixel resolution. The relative abundance of Nd was represented using the continuum-removed area of the 800 nm feature. The resulting map, highlighting hundreds of anomalous pixels, was validated through laboratory spectroscopy, surface geology, and high-resolution satellite imagery. This study marks a major advancement in REE exploration, demonstrating for the first time, the possibility of directly detecting Nd in geologic environments using the EnMAP hyperspectral satellite data. This capability can offer a fast and cost-effective method for screening Earth{\textquoteright}s surfaces for REE signature, complementing the existing exploration portfolio and facilitating the discovery of new resources.",
keywords = "Absorption feature analysis, Bastnaesite, Carbonatite, Neodymium, REE exploration, Remote sensing",
author = "Saeid Asadzadeh and Nicole Koellner and Sabine Chabrillat",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",
year = "2024",
doi = "10.1038/s41598-024-71395-2",
language = "English",
volume = "14",
journal = "Scientific reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",
}