Defense　Meteorological　Satellite　Program　Operational　Linescan　System　(DMSP-OLS)　and　Suomi　National　Polar-orbiting　Partnership　Visible　Infrared　Imaging　Radiometer　Suite　(SNPP-VIIRS)　data　are　valuable　records　of　nighttime　lights　(NTLs)　in　analyzing　socioeconomic　development.　However,　inconsistencies　between　these　data　have　severely　restricted　long　time-series　analyses.　Published　time-series　NTL　data　sets　are　not　widely　available　or　accurate　because　the　DMSP-OLS　calibration　is　inadequate　and　some　missing　data　in　the　SNPP-VIIRS　data　are　seldom　considered　for　patching.　To　address　these　issues,　we　calibrated　DMSP-OLS　data　(1992　&　x2013;2013)　by　using　a　quadratic　model　based　on　a　&　x201C;pseudo-invariant　pixel　&　x201D;　method.　Thereafter,　an　exponential　smoothing　model　was　used　to　predict　and　patch　missing　data　in　the　monthly　SNPP-VIIRS　data　(2013　&　x2013;2019).　Outliers　and　noise　were　also　removed　from　the　annual　data.　In　addition,　a　sigmoid　model　was　employed　to　generate　improved　simulated　DMSP-OLS　(SDMSP-OLS)　data　(2013　&　x2013;2019),　which　were　appended　with　the　calibrated　DMSP-OLS　data　(1992　&　x2013;2013)　to　develop　improved　DMSP-OLS-like　data　(1992　&　x2013;2019)　in　China.　Finally,　we　qualitatively　and　quantitatively　compared　these　data　with　published　NTL　data　to　examine　data　availability.　Results　showed　that　choosing　invariant　pixels　to　calibrate　DMSP-OLS　data　can　minimize　discontinuity.　The　correlation　between　the　SNPP-VIIRS　data　synthesized　by　the　patched　monthly　SNPP-VIIRS　data　and　the　official　annual　SNPP-VIIRS　data　in　2015and　2016　was　higher　than　those　of　the　two　existing　correction　methods　with　values　below　0.90.　Spatial　patterns　of　pixels　in　the　improved　SDMSP-OLS　data　in　2013　were　more　similar　with　the　DMSP-OLS　data　than　those　in　the　published　data.　Strong　correlations　likewise　existed　between　the　total　(average)　pixel　values　of　the　improved　SDMSP-OLS　data　(2013　&　x2013;2019)　and　the　DMSP-OLS　data　in　2012.　We　also　found　that　the　improved　DMSP-OLS-like　data　held　strong　linear　correlations　with　different　statistics,　the　average　values　of　which　were　0.931　and　0.654　at　the　national　and　provincial　levels,　respectively.　Meanwhile,　the　average　regression　values　between　the　two　published　data　sets　and　statistics　were　0.858/0.506　and　0.911/0.611,　respectively.　Our　study　has　proven　that　the　improved　DMSP-OLS-like　data　(1992　&　x2013;2019)　have　immense　potential　to　effectively　evaluate　socioeconomic　development　and　anthropic　activities.