Machine-Learning-Based Monitoring of Night Sky Brightness Using Sky Quality Meters and Multi-Source Remote Sensing

With the rapid pace of urbanization, light pollution has emerged as a critical environmental issue. Evaluating and managing light pollution effectively is challenging, as traditional monitoring methods often fail to capture its spatial distribution and driving factors comprehensively. To address this limitation, this study integrates Sky Quality Meter (SQM) observational data from three diverse locations—Chaozhou (China), Urumqi (China), and Ghent (Belgium)—with multi-source remote sensing data to construct predictive models of night sky brightness (NSB) using machine learning approaches. Among the tested models, the voting ensemble model demonstrated superior performance, achieving high predictive accuracy and robust generalization across diverse regional datasets. The generated local-scale NSB distribution maps reveal substantial regional variations in light pollution, highlighting the critical influence of local environmental and anthropogenic factors. By combining remote sensing and machine learning, this study offers a scalable and efficient method for evaluating and monitoring light pollution levels at regional scales. The findings underscore the value of these methods in providing actionable insights for light pollution mitigation and management strategies, supporting efforts to reduce its adverse impacts on the environment and society.