Construction and visualization analysis of urban night multispectral inversion model based on SDGSAT-1 glimmer imagery

• Multispectral data advances urban night spectral research. • Random forest model used to invert urban night spectral data. • Construct a blue light ratio inversion map for the entire city. The spectrum is a key physical quantity characterizing the urban nighttime light environment. However, due to the prevalent use of single-band observations in conventional nighttime light remote sensing, studies on urban nighttime spectral characteristics remain relatively weak. With the advancement of multispectral remote sensing, band-wise spectral retrieval has become feasible. In this study, based on SDGSAT-1 nighttime multispectral imagery and ground-based measurements, we compare the spectral ranges of human visual perception and satellite sensors, retrieve the urban nighttime RGB-band spectral distribution, and construct a light-environment inversion map and a blue-light ratio map. The results show that: (1) configuring bands with reference to the human visual spectral range is more suitable for remote sensing spectral retrieval, among which the B band exhibits the highest correlation with ground observations (correlation coefficient 0.879), followed by the R and G bands (0.700 and 0.688, respectively). (2) A comparison of six linear regression models with three machine learning models—random forest, back-propagation (BP) neural network, and support vector regression—indicates that machine learning models overall outperform linear models, while the three machine learning approaches achieve comparable accuracies, with cross-validated R 2 values of approximately 0.65–0.70. (3) Considering residual characteristics and uncertainty analysis, the random forest model is selected as the primary inversion model to retrieve the nighttime spectra of the main urban area of Dalian. The band-wise and blue-light ratio maps reveal the spatial patterns of high-luminance functional areas such as traffic corridors, commercial districts, and landscape lighting, demonstrating that multispectral nighttime light remote sensing can provide important support for urban lighting planning and sustainable urban development.