Abstract: Rocky desertification is the primary eco-environmental problem in Karst mountainous areas in southwestern China. Scientific measures must be formulated to comprehensively promote the prevention and control of rocky desertification. Remote sensing technology, which enjoys the advantages of rapid positioning, wide coverage, and economic efficiency, has become an important technical method for investigating the spatial distribution of regional rocky desertification. Therefore, this study extracted three key indices used to characterize rocky desertification information (i.e., vegetation coverage, bedrock exposure rate, and soil coverage) of the study area using the pixel unmixing method based on GF-5 hyperspectral data and the spectral index method based on Landsat8 multispectral data. The results show that information on vegetation coverage can be accurately extracted from the two types of satellite remote sensing data. However, Landsat8 multispectral data are difficult to distinguish information about exposed bedrocks from that of bare soil due to their band setting and spectral resolution. By contrast, GF-5 hyperspectral data enable the direct and effective extraction of bedrock exposure rate and soil coverage, as well as the accurate identification of mineral components such as calcite and dolomite in exposed bedrocks. The results of this study can provide a scientific and effective technical and theoretical basis for the evaluation, classification, and comprehensive control of rocky desertification.