The　SDGSAT-1　satellite,　independently　developed　and　launched　by　China,　features　a　higher　spatial　resolution　in　its　glimmer　imager　(GLI)　data　than　the　currently　widely　used　nighttime　light　(NTL)　remote　sensing　data,　making　it　possible　for　high-precision　extraction　of　urban　built-up　areas　(UBAs).　However,　recent　empirical　studies　have　revealed　that　the　higher　resolution　of　the　NTL　data　does　not　inherently　guarantee　enhanced　accuracy　of　the　UBA　extraction,　as　urban　morphology　and　city　scale　significantly　influence　the　performance.　Therefore,　comprehensively　investigating　the　UBA　extraction　performance　of　NTL　data　across　cities　with　different　morphologies　and　scales　offers　valuable　guidance　for　optimizing　urban　remote　sensing　methodologies　in　diverse　city　contexts.　Across　15　representative　cities　with　various　urban　characteristics　in　China,　this　article　conducted　a　comprehensive　evaluation　of　the　UBA　extraction　performance　of　SDGSAT-1　GLI　data　for　the　first　time,　with　the　NPP-VIIRS　NTL　data　for　comparison,　and　discussed　how　urban　morphology　and　city　scale　affect　the　performance　in　UBA　extraction.　The　results　indicated　that:　1)　In　cities　with　different　morphologies,　the　UBA　extraction　performance　of　SDGSAT-1　data　follows　from　high　to　low:　striped　cities,　clustered　cities,　and　agglomerate　cities.　In　cities　with　different　scales,　the　UBA　extraction　performance　of　SDGSAT-1　data　is　megacities　＞　super　cities　＞　medium　cities　＞　small　cities　＞　large　cities.　2)　Compared　with　NPP-VIIRS　data,　SDGSAT-1’s　performance　in　UBA　extraction　is　not　always　superior　and　varies　with　the　urban　morphology　and　city　scale　as　well　as　the　extraction　method,　which　necessitates　context-sensitive　selection　for　NTL　data　and　method　in　UBA　extraction.　©　2008-2012　IEEE.