Evaluation of the effect of degraded grassland restoration is of crucial for optimizing restoration measures and achieving sustainable utilization of grassland resources. However， current restoration evaluation mainly relies on manual ground surveys， making it difficult to meet the demands of large-scale， long-term dynamic monitoring. This study takes the restoration of degraded alpine grassland in Qilian Mountain National Park as an example. The spatial-temporal adaptive reflectance fusion model and multi-source remote sensing data were used to acquire a high spatial-temporal resolution NDVI dataset. Then the remote sensing estimation models for key grassland parameters were constructed to evaluate the restoration status of degraded grassland. The results show that （1） The models constructed based on high spatiotemporal resolution NDVI exhibited high accuracy， with R values of 0.82 and 0.89 and RMSE values of 12.02% and 32.36 g·m^-2 for the coverage and above ground biomass （AGB） models， respectively. （2） The inversion results show that grass growth conditions significantly improved after the implementation of restoration measures （P<0.05）， with coverage and AGB reached 1.32-2.17 times and 1.42-3.3 times of the values before the implementation of restoration measures， respectively. （3） Over the past 25 years， coverage and AGB increased at annual rates of 1.24% and 3.23 g·m^-2， respectively. The findings of this study provide technical support for evaluating the restoration effects of degraded alpine grasslands and offer a scientific basis for timely adjustments to restoration measures for degraded grasslands.

Evaluation of restoration effect  /  Grassland cover  /  Above ground biomass  /  Spatial-temporal variation