Studying the status of agricultural soils is one of the most important concerns in the agricultural sector. The soil organic carbon (SOC) is one of the main parameters and it plays an important role in improving soil properties. Hence, knowing this parameter is important in soil science. This study applied the pattern recognition (PR) method in predicting the SOC. Also, the ability of this method was compared with different methods such as the Radial Basis Function Network (RBF), Multilayer Perceptron Neural Network (MLP), Multiple Linear Regression (MLR) and Support Vector Regression (SVR). To compare the results, four performance criteria, namely, root mean square errors (RMSE), the Nash-Sutcliffe efficiency (NS), Willmott’s Index of agreement (WI), mean absolute error (MAE) and Taylor diagrams were used. Results indicated that the PR model performed significantly better than the MLP, MLR, SVR and RBF models for the estimation of the SOC.
