Extracting accurate soil-environment relationship is the key to digital soil mapping. Nowadays, remote sensing images have been used as the indicators of environmental factors in the process of obtaining soil-environment knowledge. However, the spectral differences in mono-temporal image are difficult to be used to distinguish soil types. In this study, we proposed a soil mapping method based on multi-temporal remote sensing images. The Sheshui River Basin in Huajiahe Town, Hongan County, Huanggang City of Hubei Province was selected as the study area, and the parent-material-type map, the multi-temporal sentinel-2 remote sensing images, and contour data were used to extract environmental factors related to soil properties. Soil environment relationships were obtained to infer the spatial distribution of soil types using the random forest algorithm. The field sampling points in the study area were used for validation, and the confusion matrix and Kappa coefficient of inferenced soil map were calculated to evaluate the map accuracy. The results demonstrated that the overall classification accuracy of the inferred soil map was as high as 86%. The soil type map obtained by inference was similar to the traditional soil map in the spatial distribution, but it could display more detailed information than the traditional soil map. This research can provide an effective alternative for updating the traditional soil map.