Inversion of Regional Economic Trend from NPP-VIIRS Nighttime Light Data Based on Adaptive Clustering Algorithm

Hao Lu, Wenqiang Qu, Shengnan Min, Jiaqi Chen and Eric Lefevre

Mathematical Problems in Engineering, 2022, vol. 2022, 1-8

Abstract: Night lighting is closely related to social and economic development. Inversion of socioeconomic parameters using nighttime light (NTL) remote sensing data is a research hot spot currently. In this paper, a calibration method based on adaptive clustering algorithm for the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data was proposed to remove background noise in the original imagery. The validity of the calibration method was evaluated through comparing the correlation between the corrected NTL data and the regional economic data. The result indicated that the NTL data obtained by this calibration method have higher correlation with the regional GDP data, and the values of R2 and the root mean square error (RMSE) were 0.8531 and 133.18, respectively. On this basis, the total nighttime light (TNL)-gross domestic product (GDP) regression model obtained from this paper was used to invert the GDP of Liaoning Province from 2012 to 2016. Using the TNL-GDP regression model established in high-quality regions to verify the fraudulent economic statistics of Liaoning Province, it can be proved that NTL data can be a reliable reference for reflecting regional economic development trends.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9266705

DOI: 10.1155/2022/9266705

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