An Adaptive Framework for Extracting Global Long-Term Urban Areas by Fusing Nighttime Light and Vegetation Information

Accurate long-term global urban monitoring is essential for understanding socio-economic dynamics. To overcome limitations of single-source data at macroscopic scales, this study proposes an automated framework that fuses nighttime light (VIIRS-DNB) and vegetation indices (MODIS NDVI). We develop the Nightlight-Vegetation Rectified Index (NVRI) to suppress blooming effects in vegetated regions and amplify urban–rural contrasts. An adaptive arid grid identification mechanism further mitigates the desert blooming issue. A local adaptive thresholding strategy based on multi-scale grids and the Kneedle algorithm replaces conventional global thresholds. With spatiotemporal post-processing, we generate an annual global urban extent product (NTL Urban, 2012–2024). National-scale validation shows high agreement with MGUP and MODIS urban products. Urban extents also exhibit stronger linear correlations with GDP and population than traditional optical products, highlighting their value for macro socio-economic analysis. Despite residual limitations from blooming effects, lighting preferences, and sensor resolution, our framework offers a reproducible, scalable approach for global urban monitoring and NTL-based socio-economic research.