Accurate and current wetland maps are critical tools for water resources management, however, many existing wetland maps were created by manual interpretation of one aerial image for each area of interest. As such, these maps do not inherently contain information about the intra- and interannual hydrologic cycles of wetlands, which is important for effective wetland mapping. In this paper, several sources of remotely sensed data will be integrated and evaluated for their suitability to map wetlands in a forested region of northern Minnesota. These data include: aerial photographs from two different times of a growing season, National Elevation Dataset and topographical derivatives such as slope and curvature, and multitemporal satellite-based synthetic aperture radar (SAR) imagery and polarimetric decompositions. We identified the variables that are most important to accurately classify wetland from upland areas and discriminate between wetland types for a forested region of northern Minnesota using the decision-tree classifier randomForest. The classifier was able to differentiate wetland from upland and water with 75% accuracy using optical, topographic, and SAR data combined, compared with 72% using optical and topographical data alone. Classifying wetland type proved to be more challenging; however, the results were significantly improved over the original National Wetland Inventory classification of only 49% compared with 63% using optical, topographic, and SAR data combined. This paper illustrates that integration of remotely sensed data from multiple sensor platforms and over multiple periods during a growing season improved wetland mapping and wetland type classification in northern Minnesota.