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Leveraging EnMAP hyperspectral data for mineral exploration: Examples from different deposit types

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Asadzadeh,  Saeid       
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, GFZ Helmholtz Centre for Geosciences;
Submitting Corresponding Author, GFZ Helmholtz Centre for Geosciences;

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Chabrillat,  S.       
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, GFZ Helmholtz Centre for Geosciences;

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5037027.pdf
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Asadzadeh, S., Chabrillat, S. (2025): Leveraging EnMAP hyperspectral data for mineral exploration: Examples from different deposit types. - Ore Geology Reviews, 186, 106912.
https://doi.org/10.1016/j.oregeorev.2025.106912


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Finding new mineral resources is essential to meet the growing demand for raw materials during the green transition. Addressing this challenge requires the adoption of advanced technologies to increase the efficiency and success rate of mineral exploration programs. One promising approach for this aim is hyperspectral remote sensing, which enables detailed and accurate mapping of surface mineralogy. The advent of high-quality hyperspectral satellite data, such as that from EnMAP, with global coverage and free accessibility, has created new opportunities for mineral exploration. To showcase the diverse applications of this technology, we conducted eight case studies covering six deposit types at various exploration stages/scales—from greenfield to brownfield, and from regional to deposit scales. These include the Dalli and Keder porphyry systems in Iran, the Collahuasi mining area in Chile, a cluster of high-sulfidation epithermal gold deposits in the El Indio Belt along the Chile-Argentina border, the Taknar volcanogenic massive sulfide (VMS) deposit in Iran, the Jabali Mississippi valley type (MVT) deposit in Yemen, the Khanneshin REE-bearing carbonatite complex in Afghanistan, and the McDermitt volcano-sedimentary lithium deposit in the US. EnMAP Level-2A reflectance data were leveraged to map the occurrences and abundances of key minerals, including white mica (muscovite-illite), kaolinite, alunite, pyrophyllite, dickite, topaz, diaspore, opal, chlorite, epidote, hectorite, calcite, dolomite, ankerite, hematite, goethite, jarosite, and bulk ferrous iron minerals. It was also employed to assess kaolinite crystallinity and to map the composition of white mica, chlorite, alunite, and hectorite, as well as solid solutions among carbonate minerals, using the mixture-tuned feature matching and the polynomial fitting techniques. The results indicate that EnMAP can deliver consistent and accurate mineralogical-compositional information, unattainable from other spaceborne multi- and hyperspectral sensors, thanks to its accurate calibration and high signal-to-noise ratio in the visible to short-wave infrared wavelengths. The mineral mapping products provided valuable insights into ore formation processes, such as sources and pathways of fluid flow, temperature and pH constraints, oxidation–reduction states, and alteration zonation patterns. In the case of REEs and lithium-bearing clays, EnMAP also enabled the direct detection of ore minerals. These studies demonstrates that spaceborne hyperspectral systems can play a crucial role in mineral exploration over exposed terrains, significantly enhancing the efficiency of exploration programs from the grassroots to the drilling stages.