Downscaling of nighttime light imagery with a spatially local estimation model using human activity-physical features

Abstract

Satellite nighttime lights (NTL) data have been extensively applied in urban studies. However, commonly-used NTL products are not able to provide fine-scale information on the intra-urban changes due to their coarse resolution (500–1000 m). Here, we propose a method for downscaling NTL data by combining the human activity-physical features adjusted NTL index with ordinary kriging approach (HPANI-OK). The proposed method was tested on the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL in 500 m, with Beijing, China. Results indicated that HPANI-OK outperformed the other approach (human activity-water features adjusted NTL index-OK and vegetation adjusted NTL urban index-OK) with a remarkable Pearson correlation coefficient (0.92), root mean square error (6.54 nW∙cm−2∙sr-1) and structural similarity (0.23) in simulating 30 m Downscaled VIIRS (DVIIRS) NTL. The HPANI-OK method significantly effectively addresses the blooming issue of raw VIIRS NTL, improves the texture similarity between the DVIIRS NTL and the reference NTL, enhances the NTL variability from artificial areas to non-artificial areas. Furthermore, the scaling effect is noticeable in simulating DVIIRS NTLs at two target resolution, i.e., 30 m and 100 m. Larger spatial differences between the initial and target resolutions weaken the pixel consistency between DVIIRS NTL and raw VIIRS NTL. However, they enhance the texture similarity between DVIIRS NTL and reference NTL. Given its high accuracy and detailed texture, HPANI-OK may be a straightforward and effective technique for downscaling NTL data in other regions and various remote sensing NTL sensors.