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Saturation Correction for Nighttime Lights Data Based on the Relative NDVI

Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518075, China
Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(7), 759;
Received: 3 June 2017 / Revised: 8 July 2017 / Accepted: 21 July 2017 / Published: 22 July 2017
PDF [10270 KB, uploaded 24 July 2017]


DMSP/OLS images are widely used as data sources in various domains of study. However, these images have some deficiencies, one of which is digital number (DN) saturation in urban areas, which leads to significant underestimation of light intensity. We propose a new method to correct the saturation. With China as the study area, the threshold value of the saturation DN is screened out first. A series of regression analyses are then carried out for the 2006 radiance calibrated nighttime lights (RCNL) image and relative NDVI (RNDVI) to determine a formula for saturation correction. The 2006 stable nighttime lights (SNL) image (F162006) is finally corrected and evaluated. It is concluded that pixels are saturated when the DN is larger than 50, and that the saturation is more serious when the DN is larger. RNDVI, which was derived by subtracting the interpolated NDVI from the real NDVI, is significantly better than the real NDVI for reflecting the degree of human activity. Quadratic functions describe the relationship between DN and RNDVI well. The 2006 SNL image presented more variation within urban cores and stronger correlations with the 2006 RCNL image and Gross Domestic Product after correction. However, RNDVI may also suffer “saturation” when it is lower than −0.4, at which point it is no longer effective at correcting DN saturation. In general, RNDVI is effective, although far from perfect, for saturation correction of the 2006 SNL image, and could be applied to the SNL images for other years. View Full-Text
Keywords: DMSP/OLS; nighttime lights; saturation correction; relative NDVI DMSP/OLS; nighttime lights; saturation correction; relative NDVI

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wang, Z.; Yao, F.; Li, W.; Wu, A.J. Saturation Correction for Nighttime Lights Data Based on the Relative NDVI. Remote Sens. 2017, 9, 759.

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