Accurate and timely information regarding the location and area of major crop types has significant economic, food, policy, and environmental implications. Both hard and soft classification methods are used throughout the growing season to generate cropland distribution maps using multiple remotely sensed data. Hard classification models (HCMs) yield good results in large homogeneous areas where pure pixels are dominant, but they fail in fragmented areas where mixed pixels are dominant. Conversely, soft classification models (SCMs) are thought to have greater accuracy in fragmented areas than in regions with pure pixels. To take advantage of both methods, we develop a hard and SCM (HSCM) based on existing HCMs and SCMs, and test it using data from simulated images as well as actual satellite data from southeast Beijing, China. The model assessment was performed using three statistical metrics at scales ranging from 1 $\times$ 1 to 10 $\times$ 10 pixels. The results reveal that the HSCM has the highest classification accuracy and produces more reasonable cropland distribution maps than those produced by either HCMs or SCMs. Moreover, the theory and methods employed in developing the HSCM provide a unifying framework for mapping land cover types, and they can be applied to different HCMs and SCMs beyond those currently in use.