The concentration of nitrogen (N) in foliage often limits photosynthesis and can influence a number of important biogeochemical processes. For this reason, methods for estimating foliar %N over a range of scales are needed to enhance understanding of terrestrial carbon and nitrogen cycles. High spectral resolution aircraft remote sensing has become an increasingly common tool for landscape-scale estimates of canopy %N because reflectance in some portions of the spectrum has been shown to correlate strongly with field-measured %N. These patterns have been observed repeatedly over a wide range of biomes, opening new possibilities for planned Earth observation satellites. Nevertheless, the effects of spectral resolution and other sensor characteristics on %N estimates have not been fully examined, and may have implications for future analyses at landscape, regional and global scales. In this study, we explored the effects of spectral resolution, spatial resolution and sensor fidelity on relationships between forest canopy %N and reflectance measurements from airborne and satellite platforms. We conducted an exercise whereby PLS, simple and multiple regression calibrations to field-measured canopy %N for a series of forested sites were iteratively performed using (1) high resolution data from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) that were degraded spectrally from 10nm to 30nm, 50nm, 70nm, and 90nm bandwidths, and spatially from 18m to 30m and 60m pixels; (2) data representing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) spectral bands simulated with data from AVIRIS; and (3) actual data from Landsat and MODIS. We observed virtually no reduction in the strength of relationships between %N and reflectance when using coarser bandwidths from AVIRIS, but instead saw declines with increasing spatial resolution and loss of sensor fidelity. This suggests that past efforts to examine foliar %N using broad-band sensors may have been limited as much by the latter two properties as by their coarser spectral bandwidths. We also found that regression models were driven primarily by reflectance over broad portions of the near infrared (NIR) region, with little contribution from the visible or mid infrared regions. These results suggest that much of the variability in canopy %N is related to broad reflectance properties in the NIR region, indicating promise for broad scale canopy N estimation from a variety of sensors.