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Remote Sens. 2017, 9(7), 673;

GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery

1,2,3, 3,4,*, 1,2,3,4, 1,2, 3,5 and 3,5
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China
University of Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
Academic Editors: Qihao Weng, Guangxing Wang, George Xian, Hua Liu and Prasad S. Thenkabail
Received: 27 April 2017 / Revised: 27 June 2017 / Accepted: 29 June 2017 / Published: 1 July 2017
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
PDF [9618 KB, uploaded 1 July 2017]


Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas with different natural or humanistic attributes. Data from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar-orbiting Partnership (NPP) satellite, are capable of estimating GDP, but few studies have been conducted for mapping GDP at pixel level and further pattern analysis of economic differences in different regions using the VIIRS data. This paper produced a pixel-level (500 m × 500 m) GDP map for South China in 2014 and quantitatively analyzed economic differences among diverse geomorphological types. Based on a regression analysis, the total nighttime light (TNL) of corrected VIIRS data were found to exhibit R2 values of 0.8935 and 0.9243 for prefecture GDP and county GDP, respectively. This demonstrated that TNL showed a more significant capability in reflecting economic status (R2 > 0.88) than other nighttime light indices (R2 < 0.52), and showed quadratic polynomial relationships with GDP rather than simple linear correlations at both prefecture and county levels. The corrected NPP-VIIRS data showed a better fit than the original data, and the estimation at the county level was better than at the prefecture level. The pixel-level GDP map indicated that: (a) economic development in coastal areas was higher than that in inland areas; (b) low altitude plains were the most developed areas, followed by low altitude platforms and low altitude hills; and (c) economic development in middle altitude areas, and low altitude hills and mountains remained to be strengthened. View Full-Text
Keywords: GDP estimation; VIIRS; nighttime light; geomorphological types; South China GDP estimation; VIIRS; nighttime light; geomorphological types; South China

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Zhao, M.; Cheng, W.; Zhou, C.; Li, M.; Wang, N.; Liu, Q. GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery. Remote Sens. 2017, 9, 673.

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