It　is　still　difficult　to　obtain　high-resolution　and　fast-updated　NDVI　data,　and　spatiotemporal　fusion　is　an　effective　means　to　solve　this　problem.　The　purpose　of　this　study　is　to　carry　out　the　comparative　analysis　and　comprehensive　trade-off　of　spatiotemporal　fusion　models　for　NDVI　generation　and　to　provide　references　for　scholars　in　this　field.　In　this　study,　four　spatiotemporal　fusion　models　(STARFM,　ESTARFM,　FSDAF,　and　GF-SG)　were　selected　to　carry　out　NDVI　image　fusion　in　grassland,　forest,　and　farmland　test　areas,　and　three　indicators　of　root　mean　square　error　(RMSE),　average　difference　(AD),　and　edge　feature　richness　difference　(EFRD)　were　used.　A　detailed　evaluation　and　analysis　of　the　fusion　results　and　comprehensive　trade-off　were　carried　out.　The　results　show　that:　(1)　all　four　models　can　predict　fine-resolution　NDVI　images　well,　but　the　phenomenon　of　over-smoothing　generally　exists,　which　is　more　serious　in　high-heterogeneity　areas;　(2)　GF-SG　performed　well　in　the　evaluation　of　the　three　indicators,　with　the　highest　comprehensive　trade-off　score　(CTS)　of　0.9658.　Followed　by　ESTARFM　(0.9050),　FSDAF　(0.8901),　and　STARFM　(0.8789);　(3)　considering　the　comparative　analysis　and　comprehensive　trade-off　results　of　the　three　test　areas　and　the　three　indicators,　among　the　four　models,　GF-SG　has　the　best　accuracy　in　generating　NDVI　images.　GF-SG　is　capable　of　constructing　NDVI　time　series　data　with　high　spatial　and　temporal　resolution.