The percent cover of vegetation canopies is an important variable for many land-surface biophysical and biogeochemical models and serves as a useful measure of land cover change. Remote sensing methods to estimate the subpixel fraction of vegetation canopies with spectral mixture analysis (SMA) require knowledge of the reflectance properties of major land cover units, called endmembers. However, variability in endmember reflectance across space and time has limited the interpretation and general applicability of SMA approaches. In this study, a subpixel vegetation cover of coniferous forests in Oregon, United States, was successfully estimated by employing shortwave infrared reflectance measurements (SWIR2 region, 2080--2280 nm) collected by the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS). The approach presented here, referred to as AutoSWIR <Asner and Lobell, 2000>, was originally developed for semiarid and arid environments and exploits the low SWIR2 variability of materials found in most ecosystems. SWIR2 field spectra from Oregon were compared with spectra from an arid systems database, revealing significant differences only for soil reflectance. However, SWIR2 variability remained low, as indicated by field spectra and principal component analysis, and AutoSWIR was then modified to use coniferous forest spectra collected in Oregon. Subsequent high spatial resolution estimates of forest canopy cover agreed well with estimates from low-altitude air photos (rms=3%), demonstrating the successful extension of AutoSWIR to a coniferous forest ecosystem. The generality of AutoSWIR facilitates accurate estimates of vegetation cover that can be automatically retrieved from SWIR2 spectral measurements collected by forthcoming spaceborne imaging spectrometers such as NASA's New Millenium Program EO-1 Hyperion. These estimates can then be used to characterize landscape heterogeneity important for land-surface, atmospheric, and biogeochemical research. ¿ 2001 American Geophysical Union