The high level of success of estimating photosynthetic vegetation from multispectral satellite sensors at regional scales has not been repeated for non-photosynthetic vegetation and bare ground. Therefore regional scale estimates of total vegetation from multispectral sensors are largely underestimated with implications for a wide range of agricultural and environmental applications. Recent research using simulated data showed that the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) had the potential to provide reliable estimates of bare ground and total vegetation. This study built on that research and found that estimates of bare ground retrieved from ASTER short-wave infrared imagery using linear spectral unmixing correlated well with field measurements (RMSE < 0.1, r2> > 0.7). Image endmember libraries required for spectral unmixing were extracted from the image data using a combination of field knowledge and the lignin and cellulose absorption index. The most reliable results were found by applying a sum-constraint to the unmixing models and tying the signatures at wavebands that corresponded to cellulose or clay-hydroxyl absorption features. The results of this research show that ASTER can improve the estimates of total vegetation extracted from satellite imagery for environmental studies at regional scales.