Although wetlands are widely recognized for thier important role in providing ecosystem services, their abundance, spatial extent, and condition remain poorly constrained and at-risk of decline. Accurate mapping and monitoring are therefore essential for their protection. However, distinguishing swamps from upland forests and shrublands is especially challenging because optical sensors cannot detect water and/or saturated soil under dense canopies. Synthetic Aperture Radar (SAR) offers distinct advantages in this regard: (1) under certain conditions, microwaves can penetrate vegetation and provide a strong backscattered signal from double bounce when surface water or very wet soil are present, and (2) microwaves can penetrate clouds, providing an opportunity to monitor changes in moisture or the extent of flooding through time. In spite of these advantages, users may still find it difficult to know which wavelengths, incidence angles, polarization states, and times of year can be used to detect swamps because of the complexity of choices, and some confusing and conflicting results presented in the literature. The goal of this research was therefore to better elucidate the impacts of sensor and environmental characteristics on the seasonal backscattering behaviour observed in and separability between swamps and dry, upland forests and shrublands, as well as determine the need for additional ancillary data like digital elevation models and derivatives to improve mapping accuracy. Using SAR data from three sensors with two different wavelengths, various polarization states, and a range of incidence angles we: (1) investigate the drivers of variations in seasonal trends and the frequency and timing of changes among different SAR time series, and assess their impact on separability, (2) quantify the importance of acquisition timing, type, number of derivatives on the accuracy of Random Forest models. Our results show that a common pre-conception that longer wavelengths are preferred for distinguishing flooded versus upland forests and shrublands has proven overly general, that data acquired before leaf flush in the spring provides superior results, and that DEM data only provides an advantage when using sub-optimal SAR data.