Lithium quantification based on random forest with multi-source geoinformation in Coipasa salt flats, Bolivia

• PCA on the geoinformation in the RF model improved the establishment of lithium elements. • MLAs are useful model-based tools for GIS-based lithium mapping. • Soil properties are useful to use as a covariable in lithium prediction models. • Atomic Absorption Spectroscopy results can be a variable in mineral prediction. • Satellite images cannot contain water in salt flats areas for lithium mapping. With the widespread use of Lithium (Li) batteries, there is an urgent demand to explore Li deposits. This study aims to apply efficient remote sensing methodologies with multi-resolution images of Sentinel-2, ASTER, and JILIN GP and soil geoinformation to locate Li by mapping its alterations and directly identifying the minerals that contain it. The principal component analysis (PCA) is applied to the satellite bands to gain an initial understanding and analysis of the temporal and spatial behaviour in the Coipasa salt flats in western Bolivia. Then, we employed the Random Forest (RF) method for defining repeatable mineral exploration targets and predictive modelling of mineral prospecting using geographical information system (GIS) tools. Predictive maps are created that contemplate the source, sediment transport, and chemical deposition processes crucial to produce this mineral. The predictive models used satellite bands and physical and chemical soil grid maps; the training data is extracted from sixteen soil and salt samples evaluated using the results of a laboratory test utilizing Atomic Absorption Spectrophotometry. The results demonstrate the high reliability of our predictive models: the values of the Area Under the Curve (AUC) of the Receiver Operating Characteristic Curve (ROC) are between 0.90 and 0.92. The results also show that the combination of PCA on satellite images and terrain information, such as physical, chemical, and morphological properties of the terrain, improves the model to predict the formation of Li ore within the Coipasa salt flats.