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　(1992-2013)　by　using　a　quadratic　model　based　on　a　＇pseudo-invariant　pixel＇　method.　Thereafter,　an　exponential　smoothing　model　was　used　to　predict　and　patch　missing　data　in　the　monthly　SNPP-VIIRS　data　(2013-2019).　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　(2013-2019),　which　were　appended　with　the　calibrated　DMSP-OLS　data　(1992-2013)　to　develop　improved　DMSP-OLS-like　data　(1992-2019)　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　2015　(　R^{2}　=0.931　)　and　2016　(　R^{2}　=0.930　)　was　higher　than　those　of　the　two　existing　correction　methods　with　R^{2}　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　(2013-2019)　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　R^{2}　values　of　which　were　0.931　and　0.654　at　the　national　and　provincial　levels,　respectively.　Meanwhile,　the　average　regression　R^{2}　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　(1992-2019)　have　immense　potential　to　effectively　evaluate　socioeconomic　development　and　anthropic　activities.　©　1980-2012　IEEE.