Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450？nm, 10？nm resolution, 2.5？m spatial resolution). Surface soil samples ( ) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with , including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands. 1. Introduction Spatial assessment of soil properties is important for understanding the dynamics of agricultural ecosystems. Site specific data can provide information that is critical to maintaining healthy soils and adequate nutrient supply for crop production, preventing losses of nutrients and sediments to the environment, and evaluating the transfer of elements such as carbon between land and atmosphere. Research has demonstrated that soil properties such as carbon content are correlated with field topography, soil texture, electrical conductivity, and soil reflectance [1–4]. A study by Venteris et al. [5] documented accumulation of carbon in low areas of fields following soil translocation from higher areas, with resulting carbon loss and soil degradation in elevated areas, and Thompson et al. [6] used soil-landscape modeling techniques to evaluate topographic distribution of soil texture and carbon content. These geographic approaches accounted for 28% to 68% of variation in measured carbon and demonstrated the complexity of environmental and management practices that affect soil characteristics. Recent research into soil health and sustainable cropping systems has demonstrated the potential of improved systems management based on knowledge of distributed soil properties [7]. Contemporary farm
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