Abstract: Grassland vegetation cover is a key indicator for assessing the health of grassland ecosystems and the effectiveness of their management. Traditionally, grassland vegetation cover has been measured through visual estimation or conventional image classification methods. These methods have drawbacks such as high subjectivity, low accuracy, and poor model generalization. In this study, we apply deep learning semantic segmentation methods to segment grassland vegetation images and estimate vegetation cover based on the segmentation results. We compare the performance of three semantic segmentation models (Unet++, DeepLabv3+, Segformer), the Canopeo model, and a classical machine learning model, Random Forest, for pixel level grassland vegetation segmentation. The results show that: (1) The Unet++ model outperforms the others, achieving a mean intersection over union (MIoU) of 0.79 and an F1-score of 0.87, while the Random Forest model performs poorly with an MIoU of 0.47 and an F1-score of 0.55. (2) For estimating vegetation cover at the image level, the Unet++, DeepLabv3+, and Segformer models show high consistency with measured vegetation coverage, with significantly better accuracy than the Canopeo and the Random Forest models. Among the deep learning semantic segmentation models, Unet++ achieves the highest estimation accuracy, with a coefficient of determination (R²) of 0.98 and a root mean square error (RMSE) of less than 3.8%. This demonstrates that deep learning models can accurately estimate grassland vegetation cover. (3) Given its superior performance, the Unet++ model was selected as the final model for estimating grassland vegetation cover. It was applied to three experimental plots in desert steppe, typical steppe, and meadow steppe regions, efficiently and accurately estimating the grassland vegetation cover in these areas. This study demonstrates that deep learning semantic segmentation models like Unet++ offer relatively high accuracy and applicability in estimating grassland vegetation coverage, providing an efficient and reliable tool for vegetation coverage estimation.