Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China

Xi Li,Huimin Xu,Chang Li,Xiaoling Chen

doi:10.3390/rs5063057

Abstract

Historically, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.

Highlights

Regional and global economic data is important to understanding the developing world, and performing an economic census plays a major role in collecting such data
The results show that the Visible Infrared Imaging Radiometer Suite (VIIRS) data can better reflect the gross regional product (GRP) than the DMSP-OLS
We found that the total nighttime light (TNL)-GRP relationship from the VIIRS data is stronger than those of the DMSP-OLS data

Summary

Introduction

Regional and global economic data is important to understanding the developing world, and performing an economic census plays a major role in collecting such data. Surveying the world economy in spatial dimensions using technical approaches as alternatives to the traditional economic census is an important and challenging task for the academic community [1,2]. Compared to the high cost of performing a traditional economic census, a remote sensing technique provides an efficient approach to survey the economy. Among the various sources of remote sensing data, nighttime light imagery has played a direct and unique role in investigating economic activities, because the artificial nighttime light can reflect the use of public lighting and commercial lighting, which are strongly associated with the state of the economy. Compared to the census approach, the mapping of nighttime light can help to investigate the economics on a large scale with very low cost; the nighttime light imagery has been used to investigate the regional economics in many countries [13,14,15,16,17]

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