Land use monitoring by satellite remote sensing can improve the capacity of ecosystem resources management. The satellite source, bandwidth, computing speed, data storage and cost constrain the development and application in the field. A novel deep learning classification method based on feature augmentation (CNNs-FA) is developed in this paper, which offers a robust avenue to realize regional low-cost and high-precision land use monitoring. Twenty-two spectral indices are integrated to augment vegetation, soil and water features, which are used for convolutional neural networks (CNNs) learning to effectively differentiate seven land use types, including cropland, forest, grass, built-up, bare, wetland and water. Results indicated that multiple spectral indices can effectively distinguish land uses with a similar reflectance, achieving an overall accuracy of 99.70%, 94.81% and 90.07%, respectively, and a kappa coefficient of 99.96%, 98.62% and 99.76%, respectively, for Bayannur, Ordos and the Hong Lake Basin (HLB). The overall accuracy of 98.18% for the field investigation demonstrated that the accuracy of the classification in wet areas and ecologically sensitive areas was characterized by significant desert–grassland interspersion.