A Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2024) Combining DMSP-OLS and NPP-VIIRS
<h1><strong>Updated to 2024! Welcome to download!</strong></h1> <p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21/V22 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p> <p> </p> <p><strong>Please refer to the paper for detailed information.</strong></p> <div> <div>Li, S., Cao, X*., Zhao, C., Jie, N., Liu, L., Chen, X., Cui, X., (2023). Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS. <em>Remote Sensing,</em> 15, 3925. <a href="https://doi.org/10.3390/rs15163925" target="_blank" rel="noopener">https://doi.org/10.3390/rs15163925</a></div> </div> <p> </p> <p><strong>Other recent publications using PCNL:</strong></p> <p>Li, S., Cao, X.*, (2024). Monitoring the modes and phases of global human activity development over 30 years: Evidence from county-level nighttime light. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 126, 103627. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jag.2023.103627" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jag.2023.103627</a></p> <div> <div> <div>Li, S., Cao, X.*, Liu, L., Li, A., (2025). Inequality of divided and shared socio-economic resources in 15-minute cities of China. <em>Geography and Sustainability, </em>100337. <a href="https://doi.org/10.1016/j.geosus.2025.100337" target="_blank" rel="noopener">https://doi.org/10.1016/j.geosus.2025.100337</a></div> </div> </div>
ShareScore
40/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 8
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 0