# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Advancements in Satellite-Based Remote Sensing
These spectral confusions are consistent with the ones reported by other authors in different regions of the world [60; 61], pointing to the need to spectrally separate the classes. In follow-up work, [PERSON] et al. [66] applied the algorithms proposed by [PERSON] et al. [63] to evaluate their performance in drawing potential exploration areas in other Li pegmaties occurrences, namely in the pegmatie fields of Aracquai and Sao Joao Del Rei in Brazil. The methods (RGB combinations, band ratios, and selective PCA) and the algorithms employed (see Appendix A) showed variable performances in the two pegmatie fields. Additionally, Li pegmatie identification was more accurate in the Sao Joao Del Rei region than in Aracquai, having the vegetation coverage in the second area negatively influenced the results according to the authors [66]. It is worth mentioning the ability of RGB combinations to discriminate Li pegmaties from both host rocks and associated tailings and mine dumps, with the exception of the Landsat-5 images [66]. Additionally, in the Sao Joao Del Rei area, the most successful band ratios were the ones proposed based on the reference spectrum of lepidolite (a Li-bearing mica): Landsat-5 3/7, Landsat-8 4/7 and Sentinel-2 4/7 [63]. Regarding the ratios proposed for spodumene, only ASTER 1/3 was able to highlight Li mineralized areas. In the Aracquai area, the spodumene based ratios were the most accurate in Li pegmatie identification. In what concerns selective PCA, the two-band subset produced better results in the Sao Joao Del Rei pegmatie field while the four-band subset worked better in Aracquai [66]. Overall, selective PCA presented the most accurate results while band rationing was the least efficient method in Li pegmatie identification [66]. On the other hand, no limitations were found in the use of Sentinel-2 images, unlike that reported in other works [63]. This may suggest that the ability to discriminate Li pegmaties using Sentinel-2 images might rely on the composition of the host rocks. Nevertheless, the results obtained were very significant, contributing to the state of the art of this field, since not only did it confirm the methodology proposed by [PERSON] et al. [63] but also showed its ability to detect other pegmatie occurrences with Li potential besides the known target areas. Currently, field validation is in progress to check if Li mineralization is in fact present in the potential areas identified. Moreover, in the Alto Ligonha province (Mozambique), [PERSON] et al. [67] used ASTER and Landsat-8 images to map hydrothermal alteration minerals associated with Li-bearing pegmaties,since several pegmaties in the region present a thick kaolinitic weathering crust. The two satellite products were used to produce land cover classification maps with the SAM supervised algorithm. Although the ASTER image classification obtained a higher overall accuracy, both products allowed to identify the spectral signatures of hydrothermal clay alteration (kaolin occurrences) associated with pegmaties [67]. This identification was also supported by published spectral libraries. Additionally, field samples of Li minerals, such as lipidolite and spodumene, were collected and analyzed using an ASD HandHeld2 spectroradiometer [67]. However, the spectroradiometer only covers the VNIR region of the spectra, so it was not possible to discriminate the two Li minerals in these wavelengths. Nonetheless, the classification results, using the target signatures collected in the satellite products, were able to identify two potential areas for future pegmatie exploration [67]. The authors also concluded that ASTER and Landsat-8 provide complementary information on mineral composition. Still in the Alto Ligonha region, different satellite products (namely ASTER, Landsat-8, and Sentinel-2) and different remote sensing algorithms were compared to target Li pegmaties [68]. The algorithms include three supervised classification methods--minimum distance, SAM, and ML--and one unsupervised algorithm: k-means clustering.