A temporally consistent global 500 m-resolution monthly VIIRS-like nighttime light dataset (1992–2024)
8 affiliations
- 1Department of Geography, The University of Hong Kong, Hong Kong, SAR, China
- 2Pengcheng Laboratory, Shenzhen 518000, China
- 3College of Land Science and Technology, China Agricultural University, Beijing 100083, China
- 4China Agricultural University (Beijing, China)
- 5Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
- 6Faculty of Business and Economics, The University of Hong Kong, Hong Kong, SAR, China
- 7Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, SAR, China
- 8University of Hong Kong (Pok Fu Lam, Hong Kong)
Abstract
Nighttime light (NTL) data serve as critical indicators of human activities and have been widely applied in urbanization monitoring and socioeconomic analyses. While the most utilized global NTL datasets are derived from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite, the inherent differences in spatial resolution and temporal coverage between these sensors present challenges for direct integration into a consistent long- term dataset. Previous studies have explored the construction of annual or aggregated NTL data, but these methods often smooth out short-term fluctuations and seasonal variations, limiting the ability to capture fine-scale temporal dynamics. Monthly NTL, on the other hand, can provide a more detailed and accurate representation of temporal variations. However, the challenge with monthly data lies in maintaining consistent spatial resolution while capturing high-frequency temporal variations tied to economic cycles and seasonal trends, with data gaps persisting, further complicating the generation of continuous, high-resolution monthly NTL datasets. To bridge this gap, we propose a super-resolution network for DMSP reconstruction, with dedicated pre- and post-processing to generate long-term monthly VIIRS-like NTL products (MVNL). Leveraging multi-modal observations, monthly VIIRS-like products are reconstructed by translating DMSP data from 1992 to 2013 using NPP-VIIRS data from 2013 to 2024 as the reference. Compared with the VIIRS NTL of Earth Observation Group (EOG), the extended dataset shows substantial agreement during the overlapping months in 2012, with a mean R2 of 0.65 and RMSE of 14.27 at the pixel scale and an even higher mean R2 of 0.96 at the city scale, underscoring the reliability of the reconstructed dataset for city-level applications. The 2012 annual composite derived from monthly data shows strong agreement with the EOG product, with R2 values of 0.72 at the pixel scale and 0.98 at the city scale. Moreover, city-level evaluation against radiance-calibrated DMSP products further verifies the reconstruction accuracy, with an R2 exceeding 0.94. Compared with existing NTL products, our dataset achieves substantial improvements in resolution, spatial calibration accuracy, and temporal continuity, establishing a continuous and trustworthy data resource. The extended monthly VIIRS-like NTL dataset for 1992–2024 is freely available online at https://doi.org/10.25442/hku.31321315.v2 (Cheng et al., 2026).
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BibTeX
@article{cheng2026temporally,
title = {{A temporally consistent global 500 m-resolution monthly VIIRS-like nighttime light dataset (1992–2024)}},
author = {Hongquan Cheng and Mengqing Geng and Xuecao Li and Shijie Li and Min Zhao and Lin Chen and Jie Wang and Peng Gong and Yuyu Zhou},
journal = {Earth system science data},
year = {2026},
month = may,
doi = {10.5194/essd-18-3449-2026},
url = {https://doi.org/10.5194/essd-18-3449-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - A temporally consistent global 500 m-resolution monthly VIIRS-like nighttime light dataset (1992–2024)
AU - Hongquan Cheng
AU - Mengqing Geng
AU - Xuecao Li
AU - Shijie Li
AU - Min Zhao
AU - Lin Chen
AU - Jie Wang
AU - Peng Gong
AU - Yuyu Zhou
PY - 2026
DA - 2026/05/21
JO - Earth system science data
DO - 10.5194/essd-18-3449-2026
UR - https://doi.org/10.5194/essd-18-3449-2026
AB - Nighttime light (NTL) data serve as critical indicators of human activities and have been widely applied in urbanization monitoring and socioeconomic analyses. While the most utilized global NTL datasets are derived from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite, the inherent differences in spatial resolution and temporal coverage between these sensors present challenges for direct integration into a consistent long- term dataset. Previous studies have explored the construction of annual or aggregated NTL data, but these methods often smooth out short-term fluctuations and seasonal variations, limiting the ability to capture fine-scale temporal dynamics. Monthly NTL, on the other hand, can provide a more detailed and accurate representation of temporal variations. However, the challenge with monthly data lies in maintaining consistent spatial resolution while capturing high-frequency temporal variations tied to economic cycles and seasonal trends, with data gaps persisting, further complicating the generation of continuous, high-resolution monthly NTL datasets. To bridge this gap, we propose a super-resolution network for DMSP reconstruction, with dedicated pre- and post-processing to generate long-term monthly VIIRS-like NTL products (MVNL). Leveraging multi-modal observations, monthly VIIRS-like products are reconstructed by translating DMSP data from 1992 to 2013 using NPP-VIIRS data from 2013 to 2024 as the reference. Compared with the VIIRS NTL of Earth Observation Group (EOG), the extended dataset shows substantial agreement during the overlapping months in 2012, with a mean R2 of 0.65 and RMSE of 14.27 at the pixel scale and an even higher mean R2 of 0.96 at the city scale, underscoring the reliability of the reconstructed dataset for city-level applications. The 2012 annual composite derived from monthly data shows strong agreement with the EOG product, with R2 values of 0.72 at the pixel scale and 0.98 at the city scale. Moreover, city-level evaluation against radiance-calibrated DMSP products further verifies the reconstruction accuracy, with an R2 exceeding 0.94. Compared with existing NTL products, our dataset achieves substantial improvements in resolution, spatial calibration accuracy, and temporal continuity, establishing a continuous and trustworthy data resource. The extended monthly VIIRS-like NTL dataset for 1992–2024 is freely available online at https://doi.org/10.25442/hku.31321315.v2 (Cheng et al., 2026).
SN - 1866-3508
SN - 1866-3516
ER -