Federal land management agencies provide stewardship over much of the rangelands in the arid and semi-arid western United States, but they often lack data of the proper spatiotemporal resolution and extent needed to assess range conditions and monitor trends. Recent advances in the blending of complementary, remotely sensed data could provide public lands managers with the needed information. We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TM and concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses to evaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Predicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlation with observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended to decline as the lag between base and prediction dates increased; however, mean absolute errors (MAE) were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectral band, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1 regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions, based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM's performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.