Reconnecting the 30-Year Timeline (1992–2023): Constructing a Consistent Global 500-m NTL Dataset Using Super-Resolution Reconstruction and Ground-Object Feature Constraints

Nighttime light (NTL) data provides an excellent opportunity for continuous spatiotemporal monitoring of global urbanization. However, in the two extensively employed NTL datasets (DMSP/OLS and NPP/VIIRS), there were also problems such as spatiotemporal inconsistencies, different spatial resolution, and inconsistent brightness with that of light-sensitive ground objects, which limited the application of NTL data. To address this issue, we proposed a framework integrating super-resolution reconstruction model and ground-object feature constraints algorithm, and generated a consistent global NTL dataset (1992–2023, 500 m), namely Tongji-NTL. First, the NTL data quality enhancement operation was performed to enhance the accuracy of NTL data. Second, a super-resolution reconstruction model was developed to convert the DMSP/OLS data (1 km) into the NPP-like data (500 m). Subsequently, a novel ground-object feature constraint algorithm was constructed to enhance the interpretability of the NTL data. The assessment showed that the proposed super-resolution model achieved superior performance. And the NTL data constrained by ground-objects feature were more sensitive to road network and water. To evaluate the performance of Tongji-NTL, we conducted evaluations and results showed that: 1) Tongji-NTL has maintained a high consistency, and the overall situation has exhibited a steady upward trend. 2) The average correlation coefficients of Tongji-NTL with four statistical indicators were significantly higher compared to original data. 3) Tongji-NTL exhibits the most extensive temporal coverage, widest spatial extent and highest level of spatial resolution by comparing with other nine NTL products. Tongji-NTL is expected to make outstanding contributions to global urbanization monitoring in the future.