# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Main Challenges  
The difficulties encountered in the previously mentioned applications are mainly related to the inherent characteristics of Li deposits, especially in the case of Li pegmatites which are characterized by low areas of exposition due to their dimensions (pegmatites are relatively narrow bodies). [PERSON] et al. [61] reported difficulties in the direct application of the spectral signatures of Li-bearing minerals obtained in the laboratory to identify similar targets in the ASTER images. The authors [61] attributed this challenge to: (i) the low spatial resolution of the images when compared to the target area; (ii) spectral noise resultant from mixture of the materials presented in a pixel; and (iii) the low spectral resolution of the ASTER sensor when compared to the laboratory spectroradiometer. [PERSON] et al. [63] highlighted that the main problem associated with the identification by the hydrothermal alteration approach is that the applied RGB combinations, band ratios, and the subsets used for selective PCA correspond to algorithms proposed for the identification of other deposit types with distinct alteration styles and mineral assemblages. Thus, in order to optimize alteration mapping associated with Li pegmatites is fundamental to characterize the associated alteration minerals and possible alteration halos in the host rocks. The knowledge on the alteration halos would allow directing the image processing methods to specific minerals and it could help to discriminate between barren and Li-mineralized pegmatites. Furthermore, the possible recognition of the associated alteration halos from satellite sensors would also allow increasing the target exposition area, allowing to overcome the pegmatite thickness problem. Based on the works conducted in the Fregeneda-Almendra area, [PERSON] et al. [78] made a preliminary attempt to identify the main constraints in Li pegmatite exploration using satellite remote sensing as being: (i) the vegetation coverage that masks the target spectral signatures; (ii) the small pegmatite size when compared to pixel size (the exposition problem); (iii) the small number of bands and/or the lack of thermal bands in some satellite sensors (the spectral resolution problem); and (iv) the spectral resemblance between the Li pegmatites and urbanized areas or agricultural fields. These challenges are in line with the difficulties identified in other pegmatite locations [60; 61; 66]. Figure 3: (**a**) Geographic distribution of the retrieved publications taking into account the affiliation country. (**b**) Geographic distribution of the analyzed publications considering the study area of the work. In some cases, the study area encompasses more than one country (Portugal/Spain). In what concerns the vegetation coverage, [PERSON] [4] stated, for other applications, that even when green vegetation is sparse, dry vegetation can negatively influence the spectral signature of each image pixel. At least in the Fregeneda-Almendra region, dry vegetation is more abundant than green vegetation. However, no attempts to minimize its influence were made (only green vegetation was masked). On the other hand, [PERSON] [4] adverted that unmixing the spectral signatures of both green and dry vegetation is preferred than masking them. For that, [PERSON] and [PERSON] [79] developed an unmixing method for hyperspectral sensors in which a vegetation corrected continuum depth is obtained. Despite multispectral products such as ASTER not being the most adequate to measure the spectral features of dry vegetation, it is still possible to obtain an approximate dry vegetation index [4]. Nonetheless, the compromise between spatial and spectral resolution is, in our opinion, the principal challenge in Li pegmatite exploration. This is best exemplified in the work that attempted to classify Sentinel-2 images using MLAs [69].