The objective of this research was to develop an improved understanding of the requirements for remote sensing derived land cover classifications and vegetation indices for modeling forest transpiration and net primary production (NPP) in a heterogeneous area. Field measurements and models were used to determine if species differentiation is required of remote sensing data in support of modeling ecosystem processes. The study site was in northern Wisconsin, USA (45.92°N 90.27°W) and covered an area of 102 km. Much of the area is a mixture of forest cover types dominated by trembling aspen (Populus tremuloides Michx.), sugar maple (Acer saccharum Marsh.), red pine (Pinus resinosa Ait.), speckled alder (Alms rugosa DuRoi), and white cedar (Thuja occidentalis L.). A diurnal model of forest transpiration using 30-minute time steps matched field measurements better than a daily model of transpiration. However, when summed over the 57 days examined, both approaches matched the field data well. Light use efficiency (LUE) derived from NPP and absorbed radiation measurements differed significantly among forest cover types. LUE was used to estimate NPP spatially based on aggregated land cover data and spatial leaf area index estimates. There was a less than 10% difference in mean NPP between using a species-specific land cover classification and a biome level classification. Spatial NPP estimates were also sensitive to spatial representation of leaf area index (LAI). The effects of land cover heterogeneity and atmospheric aerosols on four different vegetation indices were quantified. Differences between indices resulted in a variation in NPP estimates of up to 21%. This dissertation shows that remote sensing data should contain sufficient spatial and spectral information for species identification and quantification of land surface parameters that reflect local ecological functioning for use in validation and modeling studies. Future research should extend the framework used here to other regions, especially those with strong environmental gradients.