# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Advancements in Satellite-Based Remote Sensing
To achieve that goal, several methods applied to other deposit types were adapted to the Sentinel-2 images, including RGB (red, green, blue) combinations, band ratios and principal component analysis (PCA). These methodologies allowed to predict the occurrence of iron oxides and clay minerals, and to discriminate between non-altered and hydrothermally-altered zones. The authors also tried to directly identify Li pegmatites based on their spectral behavior. Using the reference spectra of spodumene (a Li-bearing mineral), the band ratio 3/8 was proposed to discriminate the presence of Li-bearing minerals. Another approach was to use a supervised image classification to map Li-bearing pegmatites. The classification was performed on the resultant bands from the application of PCA, using the land cover signature classification (LCS) and the maximum likelihood (ML) algorithms simultaneously. The reference spectral curves used to classify the image were the ones collected in training sites identified in the Sentinel-2 images. The training areas included open-pit mines exploiting Li minerals. Although the algorithm employed allowed to correctly identify the Li pegmatites in exploitation, it also generated some false positives and misclassified some metasedimentary areas. The authors [62] also highlighted the need and importance of the acquisition of mineral spectra to improve the classification output. Considering the results given in this work, [PERSON] et al. [63] presented two new different approaches: (i) identification of hydrothermal alteration minerals associated with the Li-bearing pegmatites; and (ii) direct identification of Li minerals. These methods were applied in different types of multispectralcloud-free products, namely, Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), ASTER, and Sentinel-2 MSI. The alteration mapping results differ from one image processing method to another [63]: while RGB combinations highlighted hydrothermally-altered areas near the Li-bearing pegmaties, the common band ratios applied were unsuccessful in the identification of iron oxides and clay minerals in the open-pit mines where they were described [64]. Additionally, Selective PCA, i.e., PCA applied in a subset of bands chosen based on characteristic absorption or reflectance features of the target minerals, only identified the occurrence of iron minerals in some of the open pits. The ASTER sensor was the one that showed better performance since hydroxyl-bearing minerals like clays were solely identified in the ASTER image [63], reflecting the higher mapping capabilities of ASTER due to its better spectral resolution. On the other hand, some spectral confusion with areas affected by wildfires and with agricultural fields hindered the results. In what concerns the approach to directly identify Li minerals, [PERSON] et al. [63] proposed new algorithms capable of identifying the Li pegmaties with better exposition (i.e., outcropping area) (see Appendix A). In the case of RGB combinations (ASTER 5-1-14 and 2-1-13; Landsat-8 7-3-11 and 2-1-11; Landsat-5 7-2-6 and 2-1-6), it was possible to discriminate the Li-bearing pegmaties from the host rocks (metapelites) [63]. However, this discrimination could not be achieved using Sentinel-2 images due to the lack of TIR bands and the importance of this region of the electromagnetic spectrum in silicate minerals [55; 65] like the Li-bearing silicates (spodumene and petalite) that occur in the Fregeneda-Almendra area. The proposed band ratios (ASTER 7/6 and 1/3, Landsat-8 3/5, Landsat-5 2/4, Sentinel-2 3/8) and selective PCA based on subsets of two and four bands were also able to highlight the Li pegmaties, isolating the related spectral inputs, although the authors recommend the use of the two-band subset for PCA since it reduces unnecessary noise [63]. Despite the success in Li pegmatie discrimination, all methods manifested spectral confusion with urbanized areas and agricultural fields.