This　study　evaluates　the　comparative　performance　of　spatiotemporal　fusion　and　time-series　fitting　methods　for　constructing　high-spatiotemporal-resolution　remote　sensing　time-series　data.　Due　to　in-class　similarity　of　fusion　methods　and　fitting　methods,　we　employ　the　Fit-FC　(Fitting,　spatial　Filtering,　and　residual　Compensation)　model　as　a　representative　fusion　method　and　the　linear　harmonic　fitting　model　as　a　representative　fitting　method.　Both　Fit-FC　and　the　linear　harmonic　fitting　are　widely　used　for　high-spatiotemporal-resolution　time-series　data　construction,　and　we　modify　the　original　Fit-FC　model　to　enable　automatic　time-series　fusion.　To　ensure　data　representativeness,　we　use　3　years　(2019-2021)　of　Harmonized　Landsat　and　Sentinel-2　surface　reflectance　datasets　and　Terra　MCD43A4　products.　Eight　experimental　regions　are　selected　worldwide　to　guarantee　generalization　of　the　comparative　performance　between　fusion　and　fitting　methods,　covering　diverse　land-use　types　(cropland,　developed　land,　forest,　and　grassland)　and　varying　climatological　conditions.　Time-series　of　NDVI　and　surface　reflectance　are　analyzed　under　both　actual　observations　and　simulated　data-missing　scenarios.　The　constructed　time-series　data　reveals　that　(1)　the　modified　Fit-FC　and　linear　harmonic　fitting　model　achieve　excellent　performance　in　constructing　high-resolution　time-series　images;　(2)　the　fusion　method　outperforms　the　fitting　method　in　constructing　time-series　of　NDVI　and　surface　reflectance　images　in　cropland-,　forest-,　and　grassland-dominated　regions;　(3)　both　methods　achieve　comparable　performance　in　developed-dominated　regions;　(4)　the　fusion　method　is　more　robust　to　missing　data,　and　better　captures　abrupt　phenological　transitions　under　conditions　of　continuous　missing　data;　(5)　the　fitting　method　is　computationally　more　efficient,　making　it　suitable　for　large-scale　time-series　image　reconstruction.　This　study　provides　valuable　insights　for　selecting　optimal　strategies　to　generate　high-resolution　time-series　images　across　diverse　application　scenarios　and　lays　a　foundation　for　extensions　to　other　vegetation　indices　or　land　surface　variables.