How to account for endmember variability in spectral mixture analysis of night-time light imagery?

Night-Time light imagery has become a very popular data source for monitoring the intensity of human activity in urban environments. Subpixel information is required in many applications, however, the widely used low-spatial-resolution night-time light imagery suffers from the mixed-pixel problem. In this paper, using the Visible Infrared Imaging Radiometer Suite (VIIRS) data, we presented the first spectral mixture analysis (SMA) on night-time light imagery. Specifically, we proposed to define two endmembers (light and dark) for endmember selection. In order to address the severe endmember variability problem caused by the various light sources and intensities, we adopted the Bayesian SMA (BSMA) method which is based on Bayes' theorem. The results indicate that the approach can obtain subpixel light fraction accurately with an overall root mean square error (RMSE) of 0.17 and a coefficient of determination (R-2) of 0.95. Although BSMA achieved similar results with the traditional linear SMA, BSMA allows one to determine the uncertainty of the estimated fraction as a distribution.