Abstract: Land cover is closely related to local ecological environment．Remote sensing technology can quickly and accurately extract ground feature information, and plays an important role in land cover classification．Singular classification data source, mixed pixels, few quantitative remote sensing products, all leave plenty room for further improvements in existing land cover classification methods, and in the accuracy of present classifications．Landsat 8 OLI reflectance data were combined with vegetation phenological feature data (extracted by quantitative remote sensing inversion of Fractional Vegetation Cover - FVC), and the existing three land cover classification methods of neural network, support vector machine and random forest were compared．The random forest classification method showed good results．The overall accuracy of the classification method combining reflectance with vegetation feature data is 85.52%, and the Kappa coefficient is 0.8212, 3.45pct higher than the overall accuracy of land cover classification using reflectance alone, and the Kappa coefficient is increased by 0.0429．The vegetation feature data extracted by vegetation coverage can effectively improve mapping accuracy and user accuracy of cultivated land, grassland and bare land．User accuracy of woodland and water bodies was found to have increased by 7.79pct and 1.81pct respectively．Mapping accuracy of shrubs and artificial ground was found to have increased by 7.69pct and 0.59pct, respectively．Overall, extraction of land cover information combined with vegetation coverage and derived vegetation characteristics provides effective support for improving classification accuracy while being simple and easy to implement．