Biophysical variables have both direct and indirect effects on the uptake and release of carbon dioxide (CO₂) within tundra ecosystems. Arctic landscape shows high levels of spatial heterogeneity. High spatial-resolution remote sensing data has the ability to capture the fine-grain spectral response of various biophysical variables at the landscape scale. To accurately model CO₂ flux patterns using remote sensing data we first need to model the relationships between biophysical variables and their spectral response. In this study we model percent vegetation cover (PVC), aboveground biomass (AGB), and soil moisture using high spatial-resolution (IKONOS 4 m) normalized difference vegetation index (NDVI) values. At two non-overlapping Arctic landscape sites statistically robust landscape-scale sampling procedures were used to characterize the biophysical variables. NDVI values were extracted from IKONOS data, and linear bivariate regression models were calibrated and validated using a k-fold cross-validation technique. PVC and percent soil moisture produced the strongest and most consistent results (r2≥ .84 and .73, respectively). Analysis of covariance tested the use of common models for each site. The models were not coincidental—combining data from various sites should be done with caution—but illustrated parallelism in that NDVI responds to each biophysical variable equally, regardless of site.