Rangeland and seeded forage in Canada's Prairie provinces represent productive landscapes that provide multiple ecosystem services. Past efforts to map these resources at regional scales have not achieved consistently high accuracies as they are spatially variable in both ecology and management. In particular, Agriculture and Agri-food Canada needs to distinguish these land use classes from each other and from cropland in its annual national agricultural land cover inventory. Given the potential to distinguish these classes based on seasonal phenological differences, this study used multi-season Landsat 8 top-of-atmosphere reflectance data and derived vegetation and phenological indices, as well as mid-summer RADARSAT-2 data in random forest classification of two ecoregions in Alberta and Manitoba. Classification accuracy was compared for single and multi-date Landsat 8 variables, the vegetation index and phenological variable groups, RADARSAT-2 VV and VH backscatter intensity, and combined datasets. Variable importance analysis showed that spring Landsat 8 reflectance generally contributed most to class discrimination, but accuracy improved with the addition of Landsat 8 data from the other seasons. Vegetation indices and phenological variables produced similar accuracies and were deemed to not warrant the additional processing effort to derive them. RADARSAT-2 VH backscatter was the most important variable for the Manitoba study area, which is wetter with more vegetation structure variability than the Alberta study area. Backscatter intensity significantly increased overall accuracy when it was combined with one or two-season Landsat 8 data. The best overall accuracy was achieved using the three seasons of Landsat 8 and mid-summer RADARSAT-2 data, but it was not significantly better than that for two season Landsat 8 + RADARSAT-2. The methods presented in this paper provide a process for accurate and efficient classification of seeded forage, rangeland and cropland that can be applied over large areas in operational agricultural land cover inventory.