Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5675-2025
https://doi.org/10.5194/essd-17-5675-2025
Data description paper
 | 
28 Oct 2025
Data description paper |  | 28 Oct 2025

A high-quality daily nighttime light (HDNTL) dataset for global 600+ cities (2012–2024)

Zixuan Pei, Xiaolin Zhu, Yang Hu, Jin Chen, and Xiaoyue Tan

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Cited articles

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Cao, X., Chen, J., Imura, H., and Higashi, O.: A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data, Remote Sens. Environ., 113, 2205–2209, https://doi.org/10.1016/j.rse.2009.06.001, 2009. 
Chang, Y., Wang, S., Zhou, Y., Wang, L., and Wang, F.: A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing, Remote Sens., 12, 102, https://doi.org/10.3390/rs12010102, 2019. 
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Short summary
Nighttime light (NTL) data aids urban planning and disaster monitoring. Our study introduces HDNTL, a daily NTL dataset for 653 major cities (population >1 M by 2025) from 2012–2024, enhancing NASA's VNP46A2 accuracy by correcting spatial mismatches, angular effects, and filling small data holes via spatiotemporal interpolation. Validated against high-resolution NTL datasets and ground-truth records, HDNTL enables precise monitoring of nighttime human activities.
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