Enhancing nighttime light remote Sensing: Introducing the nighttime light background value (NLBV) for urban applications

Artificial light at night, as captured by nighttime light (NTL) remote sensing, typically consists of two components: static urban lighting facilities and dynamic outdoor human activities. Separating these components can improve our understanding of the mechanism underlying NTL remote sensing and broaden its applications. In this paper, we introduce the concept of Nighttime Light Background Value (NLBV) to represent NTL emitted solely by static urban lighting facilities, excluding the influence of outdoor human activities. By utilizing a random forest method, we derived the pixel-level NLBV for Shanghai from NTL data. Comparative analysis demonstrates that NLBV exhibits a stronger correlation with building density and road density compared to the original NTL data. Our empirical findings demonstrate that the definition and application of NLBV can signifi-cantly enhance NTL-based applications for extracting urban physical attributes and estimating socioeconomic variables. Firstly, the urban built-up area extracted based on NLBV outperforms the original NTL data, especially in highly urbanized. Secondly, separating static urban lighting and dynamic human activity enables a more accurate estimation of socioeconomic variables with different contributions. Moreover, our results highlight the significant potential of incorporating NLBV in NTL-based applications across various disciplines. Overall, this study demonstrates the significance of NLBV in improving the accuracy and applicability of NTL data, opening up new opportunities for research and practical applications across various domains.