Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

Remote Sensing

Kaifang Shi²  Bailang Yu²  Yixiu Huang²  Yingjie Hu¹  Bing Yin²  Zuoqi Chen²  Liujia Chen² 

[1] Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA; E-Mail:;Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, 500 Dongchuan Rd., Shanghai 200241, China; E-Mails:

关键词: NPP-VIIRS;    DMSP-OLS;    gross domestic product;    electric power consumption;    linear regression;    nighttime light data;    China;   

DOI  :  10.3390/rs6021705

来源: mdpi

【 摘 要 】

The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the GDP and EPC (which is from the country’s statistical data) at provincial- and prefectural-level divisions of mainland China. The result of the linear regression shows that R² values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland