Machine learning (ML) algorithms have shown great performance in geological remote sensing applications. The study area of this work was the Fregeneda–Almendra region (Spain–Portugal) where the support vector machine (SVM) was employed. Lithium (Li)-pegmatite exploration using satellite data presents some challenges since pegmatites are, by nature, small, narrow bodies. Consequently, the following objectives were defined: (i) train several SVM’s on Sentinel-2 images with different parameters to find the optimal model; (ii) assess the impact of imbalanced data; (iii) develop a successful methodological approach to delineate target areas for Li-exploration. Parameter optimization and model evaluation was accomplished by a two-staged grid-search with cross-validation. Several new methodological advances were proposed, including a region of interest (ROI)-based splitting strategy to create the training and test subsets, a semi-automatization of the classification process, and the application of a more innovative and adequate metric score to choose the best model. The proposed methodology obtained good results, identifying known Li-pegmatite occurrences as well as other target areas for Li-exploration. Also, the results showed that the class imbalance had a negative impact on the SVM performance since known Li-pegmatite occurrences were not identified. The potentials and limitations of the methodology proposed are highlighted and its applicability to other case studies is discussed.

Field, textural, paragenetic, and chemical data for micas from pegmatites of the Fregeneda-Almendra pegmatitic field (Central-Iberian Zone) are used to characterize and evaluate their petrogenesis. These pegmatites show a zonal distribution from barren to evolved, with an increase in degree of evolution with increasing distance from the Mêda-Penedono-Lumbrales leucogranite. Five types of evolved pegmatites have been recognized: (1) petalite-rich, (2) spodumene-rich, (3) Li-mica + spodumene-rich, (4) Li-mica-rich, and (5) cassiterite-rich pegmatites, plus six types of barren and intermediate pegmatites. Representative micas from the different pegmatite types and from the leucogranite were analyzed for major and trace elements. All micas belong to the muscovite-lepidolite series. Lithium is incorporated into Li-micas via the Li₃Al₋₁□₋₂ and Si₂LiAl₋₃ substitutions, where □ represents vacancies. The Al₄Si₋₃□₋₁ and Al₂□₁R²⁺₋₃ substitutions, where R²⁺ = (Fe + Mg + Mn), account for the compositional variability of micas from the Li-mica-free pegmatites. The Li, Rb, Cs, Be, Ta, and Nb contents of micas increase in the order: leucogranites and barren pegmatites < intermediate pegmatites < spodumene-bearing and petalite-bearing dikes < Li-mica-bearing pegmatites. The Ba content decreases in the same order, and Sn and Zn are relatively abundant in the intermediate pegmatites. These variations are consistent with rare-element enrichment via fractionation processes combined with partitioning of rare elements from the pegmatite melt into the minerals and volatile phases. However, some pegmatite types occurring in this area, such as the cassiterite-rich dikes, do not seem to form part of the same evolutionary trend.

Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration.