The cloud-free monthly composite of global nighttime light (NTL) data of the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provides indispensable indications of human activities and settlements. However, the coarse spatial resolution (15 arc sec) of NTL imagery greatly restricts its application potential. This study proposes a feasible framework to downscale NPP-VIIRS NTL using muti-source spatial variables and geographically weighted regression (GWR) method. High-resolution auxiliary variables were acquired from the Landsat 8 OLI/ TIRS and social media platforms. GWR-based downscaling procedures were consequently implemented to obtain NTL at a 100-m resolution. The downscaled NTL data were validated against Loujia1-01 imagery based on the coefficient of determination (R²) and root-mean-square error (RMSE). The results suggest that the data quality was suitably improved after downscaling, yielding higher R² (0.604 vs. 0.568) and lower RMSE (8.828 vs. 9.870 nW/cm²/sr) values than those of the original NTL data. Finally, the NTL was extendedly applied to detect impervious surfaces, and the downscaled NTL had higher accuracy than the original NTL. Therefore, this study facilitates data quality improvement of NPP-VIIRS NTL imagery by downscaling, thus enabling more accurate applications.

Suomi 国家极地轨道合作伙伴关系与可见红外成像辐射计套件 (NPP-VIIRS) 日/夜波段 (DNB) 的全球夜间光 (NTL) 数据的无云月度合成提供了人类活动和定居点不可或缺的迹象。然而，NTL 图像的粗糙空间分辨率（15 弧秒）极大地限制了其应用潜力。本研究提出了一个可行的框架，使用多源空间变量和地理加权回归 (GWR) 方法缩小 NPP-VIIRS NTL。从 Landsat 8 OLI/TIRS 和社交媒体平台获取高分辨率辅助变量。因此实施了基于 GWR 的缩减程序以获得 100 米分辨率的 NTL。² ) 和均方根误差 (RMSE)。结果表明，数据质量在缩小后得到适当改善，与原始 NTL 数据相比，产生更高的 R ²（0.604 对 0.568）和更低的 RMSE（8.828 对 9.870 nW/cm ² /sr）值。最后，将 NTL 扩展应用于检测不透水表面，缩小后的 NTL 比原始 NTL 具有更高的精度。因此，本研究通过缩小尺度促进 NPP-VIIRS NTL 图像的数据质量改进，从而实现更准确的应用。