NightFEST: A Feature-Enhanced SpatioTemporal fusion method for Nighttime light data from SDGSAT-1 GLI and NPP-VIIRS

Nighttime light (NTL) remote sensing provides an effective approach to observing human activities. The Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data capture short-term (daily) large-scale urbanization dynamics but are limited by their coarse spatial resolution (approximately 750 m), whereas the Sustainable Development Science Satellite 1 (SDGSAT-1) panchromatic band GLI NTL data provide finer spatial details (10 m) but lower temporal frequency (about 11 days). Spatiotemporal fusion presents a feasible solution to obtain NTL data with both high spatial and temporal resolutions for a more accurate depiction of human activity patterns. This study proposes a Feature-Enhanced SpatioTemporal fusion method for NTL data, namely NightFEST. NightFEST mainly integrates classification based on NTL intensity and road proximity, feature-enhanced processing, and multi-source residual distribution. Results demonstrated that NightFEST (average DISO = 0.545) achieved better fusion performance than STARFM (average DISO = 0.939) and FSDAF (average DISO = 0.744) by preserving spatial details and effectively capturing the spatial structure and brightness distribution of NTL data. Overall, NightFEST is a novel and efficient strategy for NTL spatiotemporal fusion for rapid monitoring of NTL dynamics and human activity analysis.