Accurate mapping of urban land cover is still a fundamental challenge in remote sensing communities due to the great spectral variability of urban environments. This study presents an application of multiple criteria spectral mixture analysis (MCSMA) approach to map vegetation, impervious surfaces, and soil (V-I-S) components in a highly urbanized city of Chengdu, China, using the Landsat-8 Operational Land Imager (OLI) surface reflectance product. Unlike its counterparts which rely on single indicator in the mapping process, MCSMA uses multiple indicators to better address the problem of spectral variability. Our results showed that MCSMA produced accurate V-I-S maps that well matched the actual distributions. The vegetation map presented higher accuracies than impervious surfaces and soil maps in root mean square error, mean absolute error and systematic error. Results of this study demonstrate the potential of MCSMA in accurate urban land cover mapping.