Grassland Fractional Vegetation Cover is an important ecological parameter for evaluating the health status of grassland and monitoring environmental changes. At present， the extraction of Fractional Vegetation Cover at large regional scale is mainly based on satellite remote sensing data， and Unmanned Aerial Vehicle Remote Sensing （UAVRS） data， as a supplementary means of estimating grassland cover from satellite data， can improve the accuracy of model estimation. Based on the UAVRS data and BJ3 satellite data， three vegetation cover inversion methods， namely regression analysis method， pixel dichotomy method and random forest were used to invert and validate the vegetation cover of desert grassland in Otog Banner. The results showed that the best inversion model among the regression analysis models established by the vegetation index was the quadratic polynomial model of Normalized Difference Vegetation Index （NDVI）， with R²=0.752； the R² and RMSE obtained from the random forest model directly using the waveband values of UAVRS data were 0.893 and 0.072， compared with the quadratic polynomial model of NDVI and the pixel dichotomy model， R² is improved by 0.141 and 0.151. Using the UAVRS data and the random forest method， it is possible to quickly and accurately obtain the vegetation cover of the study area on the satellite scale， which can provide support for the inversion of desert grassland vegetation cover in the large region.