Land degradation is a major environmental and social issue in temperate steppes. It is commonly determined from vegetation cover using remote sensing techniques. Steppes in eastern Mongolia are subject to resource extraction activities, such as mining and oil extraction, which affect land degradation. Recent technological progress in remote sensing has facilitated the acquirement of high-resolution data by, for example, the CubeSat satellite or unmanned aerial vehicles (UAV), providing data for detailed maps of vegetation cover and plant functional groups (PFGs). Traditional methods for monitoring vegetation cover often face typical scale issues, such as the upscaling of vegetation parameters if plot-scale field measurements are integrated to satellite data. Here, we studied the spatial distribution of PFG using machine learning and a combination of field measurements, UAV imagery (spatial resolution: 2 cm), and PlanetScope multi-temporal imagery. We provide two products at two spatial resolutions: one for UAV data, which is restricted to comparatively small areas around field measurements, and one for PlanetScope, which covers large parts of northeastern Mongolia. The results showed that the overall accuracies of UAV classification were 91–95%, whereas those of PlanetScope were 78–95%. In integrating the classified UAV data to the PlaneScope data, our proposed model minimized the scale issue that often impedes classification. Importantly, our findings revealed that the ecological effects of dirt road and railroad extended up to 60–120 m into the adjacent, otherwise less degraded steppe vegetation. A comparison between burned and unburned areas in different years indicates that wildfires affect the composition of PFG in reducing the fractional cover of graminoids and forbs, and that increasing cover of bare ground leads to a distinct and patchy mosaic of different vegetation types.