The　Sustainable　Development　Goals　Satellite　1　(SDGSAT-1),　equipped　with　the　Glimmer　Imager　(GLI),　provides　high-resolution　nighttime　light　(NTL)　data　across　multiple　spectral　bands.　Thus,　it　can　notably　monitor　human　dynamics　and　light　pollution　with　enhanced　spectral　and　spatial　resolution.　However,　cloud　cover　and　low-quality　observations　often　contaminate　the　SDGSAT-1　GLI　NTL　data,　limiting　its　effectiveness.　This　challenge　is　addressed　by　the　development　of　a　novel　method,　namely　the　SpatioTemporal　And　spectRal　gap-filling　method　for　Sdgsat-1　(STARS)　GLI　NTL　images,　which　combines　spatiotemporal　and　spectral　information　to　generate　cloud-free　NTL　images　with　satisfactory　pixel　brightness　and　continuity.　STARS　is　the　first　method　to　effectively　address　gap-filling　in　multiband　NTL　data　using　RGB　spectral　information,　even　with　irregular　time　intervals　and　limited　image　inputs.　Compared　with　traditional　methods　such　as　the　temporal　gap-filling　method　and　the　mean-weighted　gap-filling　method,　the　Cloud　Removing　bY　Synergizing　spatioTemporAL　information　(CRYSTAL)　method,　and　the　spatial　and　temporal　adaptive　reflectance　fusion　model　(STARFM),　which　do　not　specifically　account　for　the　differences　in　light　source　variations　in　multi-band　NTL　data,　STARS　demonstrates　superior　performance　(higher　R-squared　(R2)　and　lower　root-mean-square　error　(RMSE))　in　simulations　across　seven　global　cities,　demonstrating　its　effectiveness　in　filling　cloud-induced　gaps　in　multi-band　NTL　data.　On　average,　STARS　achieves　R2　values　for　the　gap-filling　results　compared　to　the　actual　values　of　0.79,　0.78,　and　0.70　in　the　RGB　bands,　respectively.　The　cloud-free　images　produced　by　STARS　extend　the　time　series　of　the　SDGSAT-1　GLI　NTL　data,　supporting　multitemporal　quantitative　analysis.　In　cloudy　regions　like　Tianjin,　China,　STARS　effectively　captures　dynamic　changes　in　NTL　before　and　after　the　Spring　Festival,　closely　matching　human　activity　patterns　from　Baidu　Maps,　both　spatially　and　temporally.　Overall,　STARS　offers　an　innovative　and　effective　approach　for　gap-filling　multiband　NTL　data,　with　potential　applications　in　similar　datasets.　©　2025　Elsevier　Inc.