The Spatiotemporal Fusion for Nighttime Light Based on SDGSAT-1 and VIIRS Data

Nighttime light (NTL) remote sensing has emerged as a pivotal tool across diverse fields such as urban expansion, socioeconomic estimation, light pollution, and energy analysis. However, existing NTL datasets with different scales exhibit shortcomings including low spatial resolution, infrequent updates, and inadequate observation timing, hindering their suitability for large-scale fine-grained applications. To address these challenges, this study designs the NTL Spatiotemporal fusion (STF) strategy, which amalgamates the strengths of multi-source NTL data, including Spatiotemporal Super Resolution (STSR) and Local NTL Recovery (LNR). Leveraging data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Sustainable Development Goals Satellite-1 (SDGSAT-1), the study constructs the spatiotemporal fusion NTL (STF-NTL) dataset to analyze NTL trends in France. The research demonstrates that, compared to alternative super resolution (SR) methods, the STSR approach excels in texture structure and evaluation accuracy. The improved module in STSR achieves superior image quality by balancing image error and human visual perception. Furthermore, STF-NTL data following LNR exhibits enhanced matching accuracy with impervious surfaces compared to other NTL datasets, aligning more closely with economic development and urbanization trends. Application of the STF-NTL dataset in France reveals an increase in NTL area in 2022 relative to 2012, rectifying statistical disparities observed with VIIRS data. Analysis indicates that regional conflicts, energy crises, and policy shifts influenced fluctuations in monthly NTL across France in 2022. The period from March to September 2022 demonstrates a strong correlation between NTL changes and the electricity market, while post-September dynamics are predominantly shaped by energy-saving policies. The STF-NTL datasets can be produced on a global scale, offering enhanced accuracy in reflecting economic, urban, and societal conditions.