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article i nfo Spatial transitions between slope positions (landform positions) are often gradual. Various methods have been developed to quantify the transitions using fuzzy slope positions. However, few studies have used the quantitative information on fuzzy slope positions in digital soil mapping or other terrain- related geographic modeling. This paper examines the use of such information for mapping soil organic matter content (SOM) within a purposive (or directed) sampling framework for predictive soil mapping. First, a fi ve slope position system (i.e., ridge, shoulder slope, back slope, foot slope, channel) was adopted and the fuzzy slope positions were der ived through an approach based on typical slope position locations. The typical slope position locations were extracted using a set of rules based on terrain attributes and domain knowledge. Secondly, the fuzzy slope positions were used to direct purposive sampling, which determined the typical SOM value for each slope position type. Typical SOM values were then combined with fuzzy slope position data to map the spatial variation of SOM using a weighted-average model - the fuzzy slope position weighted (FSPW) model - to predict the spatial distribution of SOM for two soil layers at depths of 10-15 cm and 35-40 cm in a low-relief watershed in north-eastern China. The study area comprised two portions: an area of about 4 km 2 used for model development, and an area of about 60 km 2 for model extrapolation and validation. Evaluation results show that our FSPW model produces a better prediction of the SOM than that provided by a multiple linear regression (MLR) model. Quantitative measures in the model-development area, including correlation coefficient, mean absolute error, and root mean square of error, show that the performance of the FSPW model with five modeling points from purposive sampling compares favorably with MLR results for 48 modeling points. Evidence from the quantitative assessment based on a validation set of 102 sample points in the model-extrapolation area shows that the FSPW model performs better than the MLR model, which suggests that information on fuzzy slope position was useful in aiding digital soil mapping over the area.