Broad-scale monitoring of varying moisture levels of leaves has ramifications for understanding fire potential, biogeochemistry, and ecosystem dynamics. Five different shortwave infrared (SWIR)-derived spectral indices, principal components analysis (PCA), and the tasseled cap transformation (TCT), derived from Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data, were used to quantify landscape-level foliar moisture in an ecosystem dominated by Pinus ponderosa P. & C. Lawson. Landsat TM data demonstrated stronger correlations with in situ calculations of foliar moisture than did ASTER data. The second principal component correlated strongly with ground data (r2 = 0.765). The Landsat-derived TCT wetness component was significantly correlated with ground data (r2 = 0.638) as well as a normalized difference NIR/SWIR ratio (r2 = 0.834). The spectral indices and TCT are more practical for ecosystem moisture monitoring than PCA because of the empirical nature of PCA. Based on these results, we recommend modifications to existing methods of satellite-based fire susceptibility monitoring to account for primary effects of vegetation curing and temporal variation in ground fuels.