A Downscaling Framework for Urban Nighttime Light Based on Multifactor Geographically Neural Network Weighted Regression

Downscaling nighttime light (NTL) from satellite imagery presents valuable applications at a more detailed spatial scale, especially in the realms of urban expansion and socio-economic assessment. Nevertheless, due to the complexity of geographical conditions and uncertainties in the relationships among multiple factors, the precision of NTL downscaling often encounters constraints. In this work, an incorporated multifactor geographically neural network weighted regression (MF-GNNWR) NTL downscaling framework is proposed to solve the spatial nonstationarity in high-heterogeneous urban areas, which mainly uses geographically neural network weighted regression (GNNWR) combined with multiple factors including surface physical characteristics, socio-economic attributes, and human activities to improve the accuracy of NTL, particularly in urban regions with complicated land cover. The findings illustrate that the MF-GNNWR framework displays finer downscaling accuracy on different land cover, effectively enhancing data quality. Notably, our findings underscore the pronounced influence of socio-economic and human activity factors on NTL downscaling. Comparative analysis against several alternative downscaling methodologies reveals that the MF-GNNWR framework outperforms them, exhibiting a remarkable 23.10% improvement in the Pearson correlation coefficient (r) and achieving a root-mean-square error (RMSE) of 16.95 nW/cm(2)/sr, and after residual compensation, r continue s to increase by 1.5%, while RMSE decreases by 0.157 nW/cm(2)/sr. These findings highlight the efficacy of the proposed framework in downscaling NTL, underscoring its advantages and practical utility.