As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification.

With several bands covering iron-bearing mineral spectral features, Sentinel-2 has advantages for iron mapping. However, due to the inconsistent spatial resolution, the sensitivity of Sentinel-2 data to detect iron-bearing minerals may be decreased by excluding the 60 m bands and neglecting the 20 m vegetation red-edge bands. Hence, the capability of Sentinel-2 for iron-bearing minerals mapping were assessed by applying a multivariate (MV) method to pansharpen Sentinel-2 data. Firstly, the Sentinel-2 bands with spatial resolution 20 m and 60 m (except band 10) were pansharpened to 10 m. Then, extraction of iron-bearing minerals from the MV-fused image was explored in the Cuprite area, Nevada, USA. With the complete set of 12 bands with a fine spatial resolution, three band ratios (6/1, 6/8A and (6 + 7)/8A) of the fused image were proposed for the extraction of hematite + goethite, hematite + jarosite and the mixture of iron-bearing minerals, respectively. Additionally, band ratios of Sentinel-2 data for iron-bearing minerals in previous studies were modified with substitution of narrow near infrared band 8A for band 8. Results demonstrated that the capability for detection of iron-bearing minerals using Sentinel-2 data was improved by consideration of two extra bands and the unified fine spatial resolution.