Multi-Source Corrected Nighttime Light Index for Urban Mapping in Small and Medium-Sized Cities

Monitoring urban expansion in small and medium-sized cities is essential for assessing sustainable development. Nighttime light (NTL) data, widely available and consistently capturing human activity intensity, provide a useful proxy for delineating built-up areas. However, accurately mapping urban extents from NTL remains challenging due to radiance blooming effect and limited spatial resolution. This study introduces the vegetation–water–building–thermal nighttime urban index (VWBTNUI), a multi-dimensional fusion framework that integrates NTL with spectral and thermal information to reduce spillover effects and enhance physical consistency in urban extent mapping. Using three representative inland cities in Sichuan Province, western China, VWBTNUI was compared with raw NTL data and four widely used composite indices. Results demonstrate that VWBTNUI consistently outperforms existing approaches, achieving overall accuracy (OA) values above 0.88, F1 scores above 0.80 and Kappa coefficients exceeding 0.72. Furthermore, the urban area estimates derived by VWBTNUI maintained relative area errors below 10%. The extracted urban extents also exhibit strong agreement with benchmark products. Relying on globally accessible datasets and simple pixel-level operations, VWBTNUI offers a scalable and reproducible solution for urban monitoring in data-scarce regions. By offering reliable baseline information for regional planning, the approach supports evidence-based governance and contributes to advancing Sustainable Development Goal (SDG) 11 on inclusive, safe, resilient, and sustainable cities.