Abstract The dual-flight program (April and October) for the SIR-C/X-SAR instrument aboard the shuttle Endeavor was designed expressly to acquire Synthetic Aperture Radar (SAR) imagery at two significantly different seasons. At the Michigan Forests Test Site (MFTS), the April mission occurred at the beginning of the spring thaw and the October mission occurred just prior to and during the fall color change. Four scenes are evaluated at a constant incidence angle. Seven features are extracted from the SAR data for potential use in classification using powers at different frequencies and polarizations. Given multiseason SIR-C/X-SAR imagery, there are three possible approaches in the classifier development: 1) Under the assumption that the scene does not change significantly as a function of time, develop one classification for a set of x scenes using n features, with x times the number of samples per feature; 2) ignore the multiseason availability and develop independent classifications for each scene using n features; 3) develop a true multitemporal classification where N of features equals n (number of features) times x (number of scenes). Each of these is applied using a combination knowledge-based and Bayesian classifier. Level II (roughly forest community) results show that the true multitemporal April/October classification works very well (97%), as do those for the individual scenes (>90%). A pooled classifier works poorly (April=90%, October=77%) and shows that temporal changes in phenology and moisture conditions contribute significant noise in terrain classification.