MNDISI: a multi-source composition index for impervious surface area estimation at the individual city scale

Impervious surface is a key indicator for monitoring urban land cover changes and human-environment interaction. Although the normalized difference impervious surface index (NDISI) has shown the potential to extract impervious surface areas (ISA) from multi-spectral imagery, it may lack robustness due to the spectral heterogeneity within urban impervious materials and confusion between other land covers. In this letter, a multi-source composition index is proposed to overcome the limitations of the original method. Three data sources: night-time light luminosity, land surface temperature and multi-spectral reflectance are integrated to create a modified NDISI (MNDISI), which aims to enhance impervious surfaces and suppress other unwanted land covers. Experimental results reveal that the MNDISI offers a stable and close relationship with ISA and is shown to be an effective index for mapping and estimating impervious surfaces in heterogeneous urban land cover environment.