Remote　sensing　spatiotemporal　fusion　models　blend　multi-source　images　of　different　spatial　resolutions　to　create　synthetic　images　with　high　resolution　and　frequency,　contributing　to　time　series　research　where　high　quality　observations　are　not　available　with　sufficient　frequency.　However,　existing　models　are　vulnerable　to　spatial　heterogeneity　and　land　cover　changes,　which　are　frequent　in　human-dominated　regions.　To　obtain　quality　time　series　of　satellite　images　in　a　human-dominated　region,　this　study　developed　the　Modified　Flexible　Spatial-temporal　Data　Fusion　(MFSDAF)　approach　based　on　the　Flexible　Spatial-temporal　Data　Fusion　(FSDAF)　model　by　using　the　enhanced　linear　regression　(ELR).　Multiple　experiments　of　various　land　cover　change　scenarios　were　conducted　based　on　both　actual　and　simulated　satellite　images,　respectively.　The　proposed　MFSDAF　model　was　validated　by　using　the　correlation　coefficient　(r),　relative　root　mean　square　error　(RRMSE),　and　structural　similarity　(SSIM),　and　was　then　compared　with　the　Enhanced　Spatial　and　Temporal　Adaptive　Reflectance　Fusion　Model　(ESTARFM)　and　FSDAF　models.　Results　show　that　in　the　presence　of　significant　land　cover　change,　MFSDAF　showed　a　maximum　increase　in　r,　RRMSE,　and　SSIM　of　0.0313,　0.0109　and　0.049,　respectively,　compared　to　FSDAF,　while　ESTARFM　performed　best　with　less　temporal　difference　of　in　the　input　images.　In　conditions　of　stable　landscape　changes,　the　three　performance　statistics　indicated　a　small　advantage　of　MFSDAF　over　FSDAF,　but　were　0.0286,　0.0102,　0.0317　higher　than　for　ESTARFM,　respectively.　MFSDAF　showed　greater　accuracy　of　capturing　subtle　changes　and　created　high-precision　images　from　both　actual　and　simulated　satellite　images.