||Vegetation phenology results in seasonal changes in spectral reflectance. Phenology is often underutilized in hyperspectral vegetation mapping due to a lack of repeat imagery of the same region over time. Vegetation classification at the species level could benefit from introducing phenological information to spectral libraries. New missions, such as the proposed Hysperspectral Infrared Imager (HyspIRI) mission, could potentially provide easy access to multi-temporal datasets. The availability of these data will require new approaches to building spectral libraries for species classification. This paper explores the use of Iterative Endmember Selection (IES), an automated method for selecting endmembers from an image-derived spectral library, to create single-date and multitemporal endmember libraries. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to classify vegetation species and land cover, applying single-date and multitemporal libraries to Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data acquired on five dates in the same year. Three applications of endmember libraries were tested for their ability to classify single date AVIRIS images: 1) single-date libraries that matched the image date (same-date libraries), 2) single-date libraries that were not matched to the image date (mismatched-date libraries), and 3) a combined multitemporal library containing spectra from all dates applied to all image dates. Results indicate that multitemporal, seasonally-mixed spectral libraries achieved similar overall classification accuracy compared to single-date libraries, and in some cases, resulted in improved classification accuracy. Several species had increased producer's or user accuracy using a multitemporal library, while others had reduced accuracy compared to same-date classifications. The image dates of selected endmembers from the multitemporal library were examined to determine if this information could improve our understanding of phenological spectral differences for specific species. Results demonstrate that multitemporal endmember libraries may provide a more robust alternative to single-date endmember libraries for mapping vegetation species across time and space. Multitemporal endmember libraries could provide a means for mapping species in data where phenology, climatic variability, or spatial gradients are not known in advance or may not be easily accounted for by endmembers from a single date.