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This study was conducted to develop fundamental relationships between soil properties from three representative Florida soil orders and their spectral characteristics. The ultimate goal of this work is to develop a real–time soil property sensor for use in effective farm management. A total of 270 samples collected from the three representative soil orders (Alfisol, Entisol, and Ultisol) in Florida were used for analysis. Soil samples were obtained from 0 to 15 cm depth at 15 sampling points within three specific fields of 2.0 ha each of the three soil orders at six different times of the year, assuring a wide range of sample variability in sampling times and locations. Reflectance of the soil samples was measured in the range of 400 to 2498 nm with a 2 nm increment, and the corresponding nutrient content (P, K, Ca, and Mg) along with pH and soil organic matter content was measured for each of the samples. Partial least squares analysis was used to build prediction models with a calibration data set of 180 randomly chosen samples. The remaining 90 samples were used to validate the models. The prediction models for measured soil chemical properties for the three soil orders yielded R2 values of 0.24 to 0.88. This result could be useful in the development of a soil nutrient sensor for site–specific crop management.