Normalization of VIIRS DNB images for improved estimation of socioeconomic indicators. Issue 5 (4th May 2021)

• Record Type: Journal Article
• Authors: Man, Duc Chuc; Tsubasa, Hirakawa; Fukui, Hiromichi
• Is Part Of: International journal of digital earth. Volume 14:Issue 5(2021)
• Page Start: 540
• Page End: 554
• Publication Date: 2021-05-04
• Subjects: VIIRS DNB -- seasonal factors -- stable lit -- normalization -- socioeconomic indicators; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2020.1849438
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Monthly Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) composite data are widely used in research, such as estimations of socioeconomic parameters. However, some surface conditions affect the VIIRS DNB radiance, which may create some estimation bias in certain regions. In this paper, we propose a novel normalization algorithm for VIIRS DNB monthly composite data. The aim is to normalize VIIRS radiance, collected under different surface conditions, to a reference point, so that the bias is reduced. The algorithm is based on the utilization of stable lit pixels as a reference and a nonlinear regression algorithm, to match un-normalized data to the reference data. Experimental results show that the algorithm could improve correlation ( R 2 ) between the total sum of nightlights (TOL), electric power consumption (EPC), and gross domestic product (GDP) at both a global and local scale. The algorithm could significantly diminish the seasonal component of un-normalized nightlights radiance caused by snow. The intensified nightlights radiance in sandy regions could also be reduced to a more reasonable range in comparison with other regions. Visual inspection shows that the brightness of snow-affected and sandy regions was strongly reduced after undergoing normalization.