The surface soil organic C (SOC) concentration is a useful soil property to map soils, interpret soil properties, and guide fertilizer and agricultural chemical applications. The objective of this study was to determine whether surface SOC concentrations could be predicted from remotely sensed imagery (an aerial photograph of bare surface soil) of a 115-ha field located in Crisp County, Georgia. The surface SOC concentrations were determined for soil samples taken at 28 field locations. The statistical relationship between surface SOC concentrations and image intensity values in the red, green, and blue bands was fit to a to a logarithm linear equation (R2 = 0.93). The distribution of the surface SOC concentrations was predicted with two approaches. The first approach was to apply the relationship to individual pixels and then determine the distribution; the second approach was to classify the image and then apply the relationship to determine the class boundaries and means. Eight levels of surface SOC concentrations were classified in both approaches, and there was good agreement between the two approaches with a probability value near one using a paired t-test. The predicted and measured surface SOC concentrations, based on additional soil samples from 31 field locations, were compared using linear regression (r 2 = 0.97 and r 2=0.98 for the two approaches). The surface SOC concentrations were correctly classified in 77.4 and 74.2% of cases for the two approaches. The procedures tested were accurate enough to be used for precision farming applications in agricultural fields.
