Chinese tallow (Triadica sebifera) is an invasive tree that is spreading throughout the south-eastern United States and now into the west, and in many places causing extensive change to native habitat and associated wildlife. Detecting and mapping the relative distribution of this species is important to its control and eradication. To map the relative distribution of Chinese tallow within a south-western Louisiana coastal wetland to upland environment, Earth Observing 1 (EO1) satellite Hyperion sensor hyperspectral image data were combined with a subpixel extraction method that modelled characteristic spectra from the image data without requiring a priori characteristic spectra. Because of the low percentage occurrences of Chinese tallow and high spectral covariation in the environment, unique validation and verification methods were implemented, relying on simultaneous collection of field canopy reflectance spectra and subsequent classification of canopy compositions. The subpixel extraction method produced five characteristic spectra, which we further refined to four that adequately represented the field spectra, as well as the Hyperion imaged canopy reflectance datasets. Characteristic spectra were designated as senescing foliage, cypress-tupelo trees, and trees without leaves; shadows and green vegetation; senescing Chinese tallow with yellow leaves and yellowing foliage; and senescing Chinese tallow with red leaves ('red tallow'). About 81% (n = 34) of the field and 78% (n = 33) of the Hyperion imaged characteristic spectra associated with 'red tallow' were explained by the compositions generated in the field slide classifications.