Use of fuzzy membership values obtained with the fuzzy –c means clustering (FCM) method was explored to predict soil properties over space. First, environmental factors were fuzzy-clustered. Then, soil samples were collected at modeling points in the fields to determine soil-environment relationship and fuzzy membership of each pixel point to soil type in the study region was calculated. Finally, the weighted average model was applied to fuzzy membership, thus acquiring soil properties. To evaluate effectiveness of this method, it was compared with the multiple linear regression model based on environmental factors in soil property and terrain attributes. Four indices were set up for evaluation of the performance of these two models, i.e. correlation coefficient between predicted and observed values, mean absolute error (MAE), root mean square (RMSE) and agreement coefficient (AC). To validate the method, it was applied to a study area, in the Laolai River Valley, Heshan Farm of Nenjiang County in Heilongjiang Province of China Two soil properties were chosen, i.e. A-horizon organic matter and soil thickness. Results show that the fuzzy membership weighted method produced reasonably better performance than the regression model by using less modeling points, while the linear regression model demonstrated its limitations in the study area. Although R² of the regression equations based on modeling points was high, the equation does not fit other sampling points of the area. It is, therefore, concluded that the weighted average method using fuzzy membership was an efficient way to predict soil properties, and it is more extendable than the regression approach.