Satellite-derived nighttime light images are increasingly used for various studies in relation to demographic, socioeconomic and urbanization dynamics because of the salient relationships between anthropogenic lighting signals at night and statistical variables at multiple scales. Owing to a higher spatial resolution and fewer over-glow and saturation effects, the new generation of nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB), which is located on board the Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite, is expected to facilitate the performance of nocturnal luminosity-based investigations of human activity in a spatially explicit manner. In spite of the importance of the spatial connection between the VIIRS DNB nighttime light radiance (NTL) and the land surface type at a fine scale, the crucial role of NTL-based investigations of human settlements is not well understood. In this study, we investigated the pixel-level relationship between the VIIRS DNB-derived NTL, a Landsat-derived land-use/land-cover dataset, and the map of point of interest (POI) density over China, especially with respect to the identification of artificial surfaces in urban land. Our estimates suggest that notable differences in the NTL between urban (man-made) surfaces and other types of land surfaces likely allow us to spatially identify most of the urban pixels with relatively high radiance values in VIIRS DNB images. Our results also suggest that current nighttime light data have a limited capability for detecting rural residential areas and explaining pixel-level variations in the POI density at a large scale. Moreover, the impact of non-man-made surfaces on the partitioned results appears inevitable because of the spatial heterogeneity of human settlements and the nature of remotely sensed nighttime light data. Using receiver operating characteristic (ROC) curve-based analysis, we obtained optimal thresholds of the nighttime light radiance, by equally weighting the sensitivity and specificity of the identification results, for extracting the nationwide distribution of lighted urban man-made pixels from the 2015 annual composite of VIIRS DNB data. Our findings can provide the basic knowledge needed for the further application of current nighttime light data to investigate spatiotemporal patterns in human settlements.
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