Artificial　light　at　night,　as　captured　by　nighttime　light　(NTL)　remote　sensing,　typically　consists　of　two　components:　static　urban　lighting　facilities　and　dynamic　outdoor　human　activities.　Separating　these　components　can　improve　our　understanding　of　the　mechanism　underlying　NTL　remote　sensing　and　broaden　its　applications.　In　this　paper,　we　introduce　the　concept　of　Nighttime　Light　Background　Value　(NLBV)　to　represent　NTL　emitted　solely　by　static　urban　lighting　facilities,　excluding　the　influence　of　outdoor　human　activities.　By　utilizing　a　random　forest　method,　we　derived　the　pixel-level　NLBV　for　Shanghai　from　NTL　data.　Comparative　analysis　demonstrates　that　NLBV　exhibits　a　stronger　correlation　with　building　density　and　road　density　compared　to　the　original　NTL　data.　Our　empirical　findings　demonstrate　that　the　definition　and　application　of　NLBV　can　signifi-cantly　enhance　NTL-based　applications　for　extracting　urban　physical　attributes　and　estimating　socioeconomic　variables.　Firstly,　the　urban　built-up　area　extracted　based　on　NLBV　outperforms　the　original　NTL　data,　especially　in　highly　urbanized.　Secondly,　separating　static　urban　lighting　and　dynamic　human　activity　enables　a　more　accurate　estimation　of　socioeconomic　variables　with　different　contributions.　Moreover,　our　results　highlight　the　significant　potential　of　incorporating　NLBV　in　NTL-based　applications　across　various　disciplines.　Overall,　this　study　demonstrates　the　significance　of　NLBV　in　improving　the　accuracy　and　applicability　of　NTL　data,　opening　up　new　opportunities　for　research　and　practical　applications　across　various　domains.