The　nighttime　light　(NTL)　satellite　data　have　been　widely　used　to　investigate　the　urbanization　process.　The　Defense　Meteorological　Satellite　Program　Operational　Linescan　System　(DMSP-OLS)　stable　nighttime　light　data　and　Suomi　National　Polar-orbiting　Partnership　Visible　Infrared　Imaging　Radiometer　Suite　(NPP-VIIRS)　nighttime　light　data　are　two　widely　used　NTL　datasets.　However,　the　difference　in　their　spatial　resolutions　and　sensor　design　requires　a　cross-sensor　calibration　of　these　two　datasets　for　analyzing　a　long-term　urbanization　process.　Different　from　the　traditional　cross-sensor　calibration　of　NTL　data　by　converting　NPP-VIIRS　to　DMSP-OLS-like　NTL　data,　this　study　built　an　extended　time　series　(2000-2018)　of　NPP-VIIRS-like　NTL　data　through　a　new　cross-sensor　calibration　from　DMSP-OLS　NTL　data　(2000-2012)　and　a　composition　of　monthly　NPP-VIIRS　NTL　data　(2013-2018).　The　proposed　cross-sensor　calibration　is　unique　due　to　the　image　enhancement　by　using　a　vegetation　index　and　an　auto-encoder　model.　Compared　with　the　annual　composited　NPP-VIIRS　NTL　data　in　2012,　our　product　of　extended　NPP-VIIRS-like　NTL　data　shows　a　good　consistency　at　the　pixel　and　city　levels　with　R-2　of　0.87　and　0.95,　respectively.　We　also　found　that　our　product　has　great　accuracy　by　comparing　it　with　DMSP-OLS　radiance-calibrated　NTL　(RNTL)　data　in　2000,　2004,　2006,　and　2010.　Generally,　our　extended　NPP-VIIRS-like　NTL　data　(2000-2018)　have　an　excellent　spatial　pattern　and　temporal　consistency　which　are　similar　to　the　composited　NPP-VIIRS　NTL　data.　In　addition,　the　resulting　product　could　be　easily　updated　and　provide　a　useful　proxy　to　monitor　the　dynamics　of　demographic　and　socioeconomic　activities　for　a　longer　time　period　compared　to　existing　products.　The　extended　time　series　(2000-2018)　of　nighttime　light　data　is　freely　accessible　at　https://doi.org/10.7910/DVN/YGIVCD　(Chen　et　al.,　2020).