Subpixel Detection of Lithium-Rich Pegmatites via Integrated Hyperspectral Unmixing and Multispectral Machine Learning

Abstract

Lithium(Li)-rich pegmatites are an important source of Li for the energy transition, but their detection using remote sensing data/techniques remains challenging due to subpixel occurrence. This study presents an integrated prospectivity mapping framework combining hyperspectral PRISMA data, multispectral Sentinel-2 imagery, and a fuzzy logic overlay to improve the detection of Li-rich pegmatites in the Barroso–Alvão pegmatite field (Northern Portugal). Hyperspectral processing was performed using Mixture Tuned Matched Filtering (MTMF) targeting alteration minerals associated with Li mineralization, while multispectral classification was conducted using supervised machine learning algorithms. Among the tested models, the Support Vector Machine (SVM) achieved the best generalization performance. The integration of hyperspectral mineralogical information with multispectral spatial consistency successfully identified known pegmatite occurrences and improved prospectivity mapping in complex subpixel occurrences. The proposed methodology is transferable to other regions and particularly suitable for greenfield mineral exploration.