# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Advancements in Satellite-Based Remote Sensing
The land cover classifications maps were compared as well as the performance accuracy of each classifier. Contrary to expected, the minimum distance attained the best accuracy while ML showed the worst performance [68] but no explanation to the fact is given. Perhaps these results reflect the quality of the training data. Despite the disparity between the results obtained with the different classifiers, [PERSON] et al. [68] stated that the classification maps show clay minerals, such as kaolinite and montmorillonite, resultant from pegmatie alteration. These maps were validated through field investigations, geological maps and previous works and reports [68]. The authors also noted that the satellite product that achieves the best accuracy for the SAM and minimum distance algorithm was the Sentinel-2. More work is necessary, since the results may indicate a positive contribution from the increased spatial resolution of the sensor. In a work focused on the application of image classification in Li pegmatie exploration, [PERSON] et al. [69] compared the performance between two machine learning algorithms (MLAs), support vector machine (SVM) and random forest (RF), in the Fregeneda-Almendra area, using Sentinel-2A images. For details on model selection and parametrization, the original publication should be consulted [69]. This work represents the first application of MLAs in Li pegmatie mapping. Both algorithms (SVM and RF) correctly mapped the occurrence of Li pegmaties in the open-pit mines [69]. Although SVM achieved a higher overall accuracy (98.54%), the RF model (built using class balancing strategies; overall accuracy of 97.70%) was able to correctly classify a larger number of mapped Li-bearing pegmaties [69]. Additionally, some of the areas classified by SVM and RF as Li pegmaties correspond with the regions of interest delimited in previous studies [63] or to barren mapped pegmaties. Despite the potential shown by this type of approach, these techniques still produced several false positives resultant from the spectral similarity of Li pegmaties with other lithologies, namely metasedimentary rocks [69]. This may indicate that more high spatial resolution images are needed. Moreover, other difficulties were encountered (see Section 2.2), mainly associated with the high number of classes defined in the image, having the authors [69] concluded that both SVM and RF were overfitting at a class level, despite this kind of algorithms being less prone to overfit than other machine learning techniques [70; 71; 72; 73]. This illustrates that this kind of approach can be optimized. All the developments mentioned so far concerned the application of remote sensing the hard-rock Li deposits. However, new applications in other Li deposits are emerging. One example is the \"Space enabled exploration and monitoring of Cornwall Lithium resources\" research project dedicated to Li brine exploration [74]. The project aims at targeting underground geothermal Li brines through the integration of several multispectral and SAR products to produce geological, active faulting, and vegetation maps [74]. The geological mapping will be achieved in two stages: (i) the first consists on geological mapping using Landsat-8 and Sentinel-2 images with subsequent integration of geophysical data; and (ii) the second will focus on mineral alteration mapping using multispectral (ASTER and Worldview-3) and airborne hyperspectral data (HyMAP) [74]. On its turn, SAR data, namely Sentinel-1 scenes, will be used to map in detail fault systems that may structurally control Li brines. Another approach will consist of the detection of vegetation with possible Li anomalies [74]. Another deposit type that may have big potential for remote sensing applications are metasediment-hosted Li deposits like the Valdeflores (Caceres, Spain) case where the mineralization occurs in Li-bearing micas [75; 76].