Technical Abstract: Soil hyperspectral reflectance imagery was obtained from an airborne imaging spectrometer (400 to 2450 nm with ~10 nm resolution, 2.5 m spatial resolution) flown over six tilled (bare soil) agricultural fields on the Eastern Shore of the Chesapeake Bay (Queen Anne’s county, MD). Surface soil samples from the fields (n=315) were analyzed for carbon content, particle size distribution, and fifteen agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations for each sampling location, 13 of the 19 analytes were predicted with R2 >0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and percent silt content (0.79). Comparison of 15 spectral math pre-processing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients that was used to calculate predicted maps of soil properties for each field based on imagery spectra. Image smoothing with a 3x3 low pass filter prior to spectral data extraction improved prediction accuracy, and also improved the range and distribution of values in resulting field maps. The resulting maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. Results can be used to improve precision environmental management of farmlands.