# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Advancements in Satellite-Based Remote Sensing
#### Brief Overview  
To understand the recent interest in the application of remote sensing methods in Li exploration and to provide an overall picture of the state of the research, we searched the number of publications in the Scopus bibliographic database using specific keywords (\"Remot * sens \"\" AND \"Lithium\" AND \"Mineral \"\"; \"Satellite\" AND \"Lithium\" AND \"Mineral \"\"; \"Remot * sens \"\" AND \"Lithium\"). After search refinement, title and abstract screening and eligibility check, a total of nine publications from the initial 261 were included in the analysis (representing 3.4% of the total). Despite this number being considered low for bibliographic analysis [77], it highlights the existent gap in the literature in Li exploration using remote sensing data/techniques. All documents were analyzed by type, country, and citations. The co-occurrence of keywords was also analyzed considering their frequency. The VOSviewer software was employed for network visualization of the data through clustered density maps. Table 1 presents a summary of information on the nine publications included in the analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors (Year)** & **Title** & **Citations** \\ \hline [PERSON] et al. (2019) & Constraints and potentials of remote sensing & \\  & data/techniques applied to lithium (Li) pegmatites & - \\ \hline [PERSON] et al. (2019) & Remote sensing data in lithium (Li) exploration: A new & \\  & approach for the detection of Li-bearing pegmatites & 6 \\ \hline [PERSON] et al. (2019) & Remote sensing techniques to detect areas with potential & - \\  & for lithium exploration in Minas Gerais, Brazil & - \\ \hline [PERSON] et al. (2019) & Evaluating the performance of support vector machines & \\  & (SVMs) and random forest (RF) in Li pegmatite mapping: & - \\  & Preliminary results & \\ \hline [PERSON] et al. (2018) & An earth observation framework for the lithium & - \\  & exploration & \\ \hline [PERSON] et al. (2018) & Potential of Sentinel-2 data in the detection of lithium & \\  & (Li)-bearing pegmatites: A study case & 4 \\ \hline [PERSON] et al. (2018) & Pegmatite spectral behavior considering ASTER and & \\  & Landsat 8 OLI data in Naipa and Muiane mines (Alto & 1 \\  & Ligonha, Mozambique) & \\ \hline [PERSON] et al. (2017) & Geological mapping and ore-prospecting study using & \\  & remote sensing technology in Jiajika area of Western & 1 \\  & Sichuan Province & \\ \hline [PERSON] (1982) & Remote sensing of geobotanical anomalies in mineral & \\  & exploration: a Landsat 1 processing methodology - & - \\  & Echassieres, Allier, France & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SCOPUS search results concerning publications related to Li exploration using remote sensing data and techniques. The average publication year is 2014, with eight documents (88.9%) published after 2016, which reflects the recent interest in this field. Almost all documents were published in English (7; 77.8%), with the exception of one document published in Chinese (11.1%) and another one in French (11.1%) (Figure 1a). When analyzing the type of document, five publications (55.6%) correspond to conference papers while the remaining four documents (44.4%) correspond to journal articles (Figure 1b). The publications received a total of 12 citations, with an average of 1.3 citations per document since there are four documents (44.4%) with at least one citation (Table 1). The co-occurrence of author and index keywords was also analyzed (Figure 2), being \"remote sensing\", \"lithium\", and \"granite\" the most frequent keywords occurring eight, seven, and four times, respectively. Figure 1: (a) Donut chart showing the distribution of the documents’ language. (b) Pie chart with the distribution of the documents’ type. Figure 2: Co-occurrence of author and index keywords. Each circle represents a different keyword, being the size proportional to the respective number of occurrences.