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Site-specific management is an effective method for optimizing crop production while maintaining soil quality. Management of soil quality requires measurement and mapping of observed variability across fields. One drawback is that understanding soil variability requires a large number of slow, expensive traditional soil tests. Recently, however, sensor-based approaches including reflectance spectroscopy have been proposed as quicker, easier alternatives. This study tested the ability of visible and near-infrared reflectance spectroscopy to estimate total carbon (C), total nitrogen (N), particulate organic matter (POM)-C, POM-N, and other soil quality factors. Samples were taken from 32 plots in Columbia, MO with a wide variation in topsoil depths and assumed differences in soil quality. Soil samples were scanned with a laboratory spectrometer in both field-moist and oven-dried conditions. Statistical calibrations were developed relating reflectance data to conventional lab analysis. Results showed that total C and N models were highly predictive, with R2 as high as 0.97 for C and 0.91 for N. Estimations of other soil quality factors were not as accurate, but estimates of some properties, including Mg and CEC were of good accuracy, with R2 of 0.83 and 0.74, respectively. This research showed that spectroscopic analysis of field-moist soil is a viable option for estimating several important soil quality factors.