Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

Currently, the global shift towards green energy is at the forefront of efforts introducing a new era, thus rendering exploration for critical raw materials essential. To this purpose, the utilization of advanced machine learning methods in remote sensing has emerged as a rapid and cost-effective approach. This study proposes a new methodology, utilizing Sentinel-2 satellite data, to distinguish ferronickel (Fe-Ni-) laterite from bauxite across pre-mining, mining, and post-mining occurrences worldwide. Both ores contain mineral raw materials such as nickel, iron, cobalt, and alumina and their discrimination is generally macroscopically challenging, especially when their locations are often in geographical proximity. The proposed method is based on Support Vector Machines (SVM) classification using spectral signatures of known Fe-Ni-laterite and bauxite-bearing pixels in Greece, Cuba, and Jamaica. The highest classification accuracies are obtained by combining 𝑏12 with 𝑏6 or 𝑏7 spectral bands. Comparisons with specific ore mineralogies show that 𝑏6 and 𝑏7 are strongly linked to the ferric phase, while 𝑏12 is mainly associated with the argillic mineralogies, the latter probably being the key discriminating factor between the two ores. From laboratory chemical analyses, we also establish that 𝑏12 and 𝑏6 or 𝑏7 are strongly associated with Al₂O₃ and Fe₂O₃ content correspondingly. The proposed method is accurate, it has reduced prospection costs, and it can facilitate the initial screening of broad areas by automatically characterizing whether an ore is bauxite or Fe-Ni-laterite. This underscores the methodology’s significance in ore differentiation and exploration within the context of green energy endeavors.