A temporally consistent global 500 m-resolution monthly VIIRS-like nighttime light dataset (1992–2024)

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).