Urban　built-up　areas　are　the　main　space　carrier　of　population　and　urban　activities.　It　is　of　great　significance　to　accurately　identify　urban　built-up　area　for　monitoring　urbanization　dynamics　and　their　impact　on　Sustainable　Development　Goals.　Using　only　nighttime　light　(NTL)　remote　sensing　data　will　lead　to　omission　phenomena　in　urban　built-up　area　extraction,　especially　for　SDGSAT-1　glimmer　imager　(GLI)　data　with　high　spatial　resolution.　Therefore,　this　study　proposed　a　novel　nighttime　Lights　integrate　Building　Volume　(LitBV)　index　by　integrating　NTL　intensity　information　from　SDGSAT-1　GLI　data　and　building　volume　information　from　Digital　Surface　Model　(DSM)　data　to　extract　built-up　areas　more　accurately.　The　results　indicated　that　the　LitBV　index　achieved　remarkable　results　in　the　extraction　of　built-up　areas,　with　the　overall　accuracy　of　81.25%.　The　accuracy　of　the　built-up　area　extraction　based　on　the　LitBV　index　is　better　than　the　results　based　on　only　NTL　data　and　only　building　volume.　Moreover,　experiments　at　different　spatial　resolutions　(10　m,　100　m,　and　500　m)　and　different　types　of　NTL　data　(SDGSAT-1　GLI　data,　Luojia-1　data,　and　NASA＇s　Black　Marble　data)　showed　that　the　LitBV　index　can　significantly　improve　the　extraction　accuracy　of　built-up　areas.　The　LitBV　index　has　a　good　application　ability　and　prospect　for　extracting　built-up　areas　with　high-resolution　SDGSAT-1　GLI　data.