ArticleScientific data2022
A consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Armed conflict accelerates cropland loss and food insecurity worldwide.Nature food · 2026Article
- Synergistic effects of outdoor nighttime light, air pollution, and PMBMC public health · 2025Article
- Independent and combined relationships between nighttime light exposure, air pollution, PMBMC public health · 2025Article
- Independent and combined relationships between light at night, air pollutants, PMBMC public health · 2025Article
- Global nighttime light dataset from 1992 to 2022 with focus on low-light areas.Scientific data · 2025Article
- Artificial light at night reveals hotspots and rapid development of industrial activity in the Arctic.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Charting the future of high forest low deforestation jurisdictions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Annual Impervious Surface Data from 2001-2020 for West African Countries: Ghana, Togo, Benin and Nigeria.Scientific data · 2024Article
- Precise GDP Spatialization and Analysis in Built-Up Area by Combining the NPP-VIIRS-like Dataset and Sentinel-2 Images.Sensors (Basel, Switzerland) · 2024Article
- Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya.The Science of the total environment · 2024Article
- A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning.Scientific data · 2024Article
- Uncertainties in deforestation emission baseline methodologies and implications for carbon markets.Nature communications · 2023Article
- A consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data.Scientific data · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Remote sensing of nighttime light can observe the artificial lights at night on the planet's surface. The Defense Meteorological Satellite Program's Operational Line Scan (DMSP-OLS) data (1992-2013) provide planet-scale nighttime light data over a long-time span and have been widely used in areas such as urbanization monitoring, socio-economic parameters estimation, and disaster assessment. However, due to the lack of an on-board calibration system, sensor design defects, limited light detection range, and inadequate quantization levels, the applications of DMSP-OLS data are greatly limited by interannual inconsistency, saturation, and blooming problems. To address these issues, we used the power function model based on pseudo-invariant feature, the saturation correction method based on regression model and radiance-calibrated data (SARMRC), and the self-adjusting model (SEAM) to improve the quality of DMSP data, and generated a Consistent and Corrected Nighttime Light dataset (CCNL 1992-2013). CCNL dataset shows good performance in interannual consistency, spatial details of urban centers, and light blooming, which is helpful to fully explore the application potentials of long time series nighttime light data.
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