Knowledge of the spatial extent of wetlands is important to a series of research questions and applications such as wetland ecosystem functioning, water management, and habitat suitability assessment. This study develops a practical digital mapping technique using an optical image of a Landsat thematic mapper (TM), Envisat advanced synthetic aperture radar (SAR) image, and topographical indices derived from topographic maps. An ensemble classifier based on classification tree procedure [random forests (RFs)] is applied to three different com- binations of predictors: (1) TM imagery alone (TM-only model); (2) TM imagery plus ancillary topographical data [TM + digital terrain model (DTM)]; and (3) TM imagery, ancillary topographical data and radar imagery (TM + DTM + SAR model). Accuracy assessment results indicate that the radar and topographical variables reduce classification error of marsh. The kappa coefficients for the land cover classification increases significantly as radar imagery and ancillary topographical data are added. The per-grid cell probabilities of each land-cover types are estimated based on the RFs model making use of all available predictors. A final land-cover map is generated by defining pixels as the land-cover type with the highest probabilities. Compared with a single classification and regression tree and a conventional maximum likelihood classifier, RFs produce the highest overall accuracy (72%) with a kappa coefficient of 0.6474, and marsh wetland accuracies ranging from 81.2% to 83.33%. The current study indicates that multisource data (i.e., optical, radar, and topography) are useful in the characterization of freshwater marshes and their adjacent land-cover types. The approach developed in the current study is automated, relatively easy to implement, and could be applicable in other settings over large extents.