A unique remotely sensed data set derived for a temperate mixed grassland in the central United States was used to test the comparability of a suite of satellite and aircraft sensors, and to characterize temporal variability in the normalized difference vegetation index (NDVI), retrieved surface radiant temperature (Ts), and surface biophysical variables. The temporal evolution of atmospherically corrected NDVI images through two growing seasons was found to be consistent among sensors. Maximum NDVI compositing of AVHRR data compared favourably with Landsat TM and SPOT-HRV, despite large variations in individual band reflectances with viewing geometry. Surface radiant temperatures retrieved from the various sensors, including aircraft TM-simulators, were also comparable, and in good agreement with surface measurements after radiometric calibration and atmospheric correction (2.7°C rmse). The relationship between NDVI andTswas determined largely by vegetation and environmental conditions (e.g., leaf area index and soil moisture), and was thus related to the partitioning of energy fluxes. The NDVI/Tsslopes were also affected by acquisition time, but were not significantly different among sensors over the growing season. These results suggest that data from different sensors can be used to augment spatial and temporal characteristics of datasets, when calibrated and corrected. Such capability diminishes the trade-off of spatial resolution at the expense of temporal resolution (and vice versa), thus allowing observation of short-term variations in biospheric processes.