The Orbita hyperspectral satellite (OHS) is the first commercial hyperspectral satellite in China that completed launching and networking. It can collect world-class hyperspectral data and obtain aerial hyperspectral imagery with 32 bands covering the spectrum range from 400 to 1000 nm at a 10-m resolution, which are of great significance for the quantitative analyses of remote sensing and refined recognitions of land covers. We explore the potentiality of the OHS image in land cover classification (LCC). Taking the Pearl River Delta region as the study area, we selected five feature indices from OHS data, i.e., original bands (OBs), vegetation indices (VIs), water indices (WIs), red edge indices (REIs), and gray-level co-occurrence matrix (GLCM) textures for the LCC. Then, data combination schemes were intended to analyze and compare the performance of different feature indices on the accuracies of the LCC. Last, feature optimization was performed on all input variables to determine the optimal variables combination to increase the accuracy and efficiency of the LCC. The random forest classifier was adopted in the above schemes, and the method of mean decrease in accuracy was used to determine the importance of the variables. The results show that (1) refined accuracy of LCC was obtained using only OBs; in addition, REIs can further improve the classification accuracy significantly. (2) The optimal variables combination achieves the highest classification accuracy (OA  =  93.21  %   and kappa coefficient  =  0.91), and the user’s and producer’s accuracies exceeded 90% for most land cover categories. (3) Variable importance analyses show that the importance of both red-edge bands and REIs were greater than those of near-infrared bands and VIs for the LCC. The importance ranking of different indices from high to low was REIs > OBs > GLCM > WIs > VIs. This research demonstrates the potentialities and values of the OHS image for the application of LCC.