Foliar nitrogen (N) in plant canopies is central to a number of important ecosystem processes and continues to be an active subject in the field of remote sensing. Previous estimates of foliar N at the landscape scale have primarily focused on intact forests and grasslands using aircraft imaging spectrometry and various techniques of statistical calibration and modeling. The present study extends this work by examining the potential to estimate the foliar N concentration (%N) of residential, agricultural and other cultivated grassland areas within a suburbanizing watershed in southeastern New Hampshire. These grasslands occupy a relatively small fraction (17.5%) of total land area within the study watershed, but are important to regional biogeochemistry and are highly valued by humans. In conjunction with ground-based vegetation sampling (n=20 sites with 54 sample plots), we developed partial least squares regression (PLSR) models for predicting mass-based canopy %N across management types using input from airborne and field-based imaging spectrometers. Models yielded strong relationships for predicting canopy %N from both ground- and aircraft-based sensors (r2=0.76 and 0.67, respectively) across sites that included turf grass, grazed pasture, hayfields and fallow fields. Similarities in spectral resolution between the sensors used in this study and the proposed HyspIRI mission suggest promise for detecting canopy %N across multiple forms of managed grasslands, with the possible exception of areas containing lawns too small to be captured with HyspIRI's planned 60m spatial resolution.
