Fractional vegetation coverage (FVC) is one of the important indicators for grassland degradation evaluation,and real-time,fast and accurate FVC acquisition is the basis for grassland degradation evaluation.This paper proposes a 3D-ResNet18 deep learning coverage extraction method using unmanned aerial vehicle (UAV) hyperspectral remote sensing images as the data source,compares this method with the regression model method and the ResNet18 classical deep learning method,and validates the extraction accuracy.The results show that the proposed 3D-ResNet18 method shows a better extraction effect on desert grassland FVC,with an overall estimation accuracy of 97.56%,which is 8.32%,5.92%,2.20%,2.14% and 1.87% higher compared to NDVI,SAVI,G_CR_NDVI,G_CR_ SAVI and ResNet18, respectively.The foundation for high-precision and efficient statistics of desert grassland FVC information is laid.