# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Advancements in Satellite-Based Remote Sensing  
Despite the latest interest in this topic, the first application of remote sensing to Li mineralizations started in 1982. [PERSON] [59] conducted a multitemporal study (from 1975 to 1976) using Landsat Multispectral Scanner System (MSS) images to detect geobotanical anomalies caused by dispersal halos of metals, such as lithium, tungsten, and arsenic, associated with the Collete granite and the Beauvoir domes. To extract the spectral signature of the circular anomaly, band ratios 7/5 and 7/6 (band 5: 0.6 to 0.7\(\upmu\)m; band 6: 0.7 to 0.8 \(\upmu\)m; band 7: 0.8 to 1.1 \(\upmu\)m) were applied to the images. Comparing the spectral anomalies of the vegetation with the soil geochemical maps, the author concluded that there was a clear correspondence between spectral and geochemical anomalies. However, such spectral anomalies could be related to the presence of the other metals in the vegetation's leaves and not necessarily due to the presence of Li. Over a decade ago, [PERSON] et al. [60] used ASTER images for a trial mapping of Li-bearing pegmatites in the Vale do Jequitinhonha region, Brazil. The authors used visible, near-infrared (VNIR), and SWIR data to evaluate the spectral signatures of gem- and Li pegmatites, and related alteration minerals. For that, the target spectra were determined through the estimation of the pixel purity index (PPI) either directly in the atmospherically-corrected images or after applying a minimum noise fraction (MNF) transformation of the data. PPI was estimated since the mean spectra of each sample group, where the pixel samples were identified in ASTER images, failed as reference spectra. The obtained reference spectra were used to classify the ASTER image using the spectral angle mapper (SAM) and the mixture-tuned matched filtering (MTMF), having the last supervised method outperform the first since SAM was also sensible to roads, agricultural fields, or areas around the main drainage lines. [PERSON] et al. [61] applied a similar methodology in the identification of Li minerals' spectral signatures in view of future identification of unknown ore deposits in the Vale do Jequitinhonha region, Brazil. To achieve that goal, reference spectra of Li-bearing minerals, as well as associated alteration minerals and soils, were collected in the laboratory. However, only the open-pit mines delimited in image data were used as training areas after the spectra obtained in the laboratory failed to serve as a reference for the classification process. Similar to before, an MNF transformation was applied to the ASTER VNIR and SWIR bands in order to segregate the noise. Statistical analysis was applied to the MNF to determine the reference pixels with the highest PPI, from which tree reference signatures were selected. Afterward, two supervised classification methods were employed, SAM and MTMF, only where the location of metasediment outcrops, while the other lithologies where masked. Although both methods were able to correctly identify the spectral features of pegmatite intrusions, the SAM classifier also mapped other features, like roads or agricultural fields, while MTMF only identified a small number of pixels to have similar spectral behavior to the reference spectra. The solution proposed by [PERSON] et al. [61] was to produce a final target potential map through the Boolean intersection of the results obtained from the application of MTMF and SAM techniques. Another remote sensing approach to Li pegmatite exploration was made recently in the Fregeneda-Almendra (Salamanca, Spain-Vila Nova de Foz Coa, Portugal) pegmatite field. [PERSON] et al. [62], in a preliminary stage, presented the potential of the Sentinel-2 Multispectral Instrument (MSI) in Li mapping. The study attempted to identify Li pegmatites mainly based on the recognition of the associated alteration halos.