Purpose: Soil depth is a soil property that influences land use, land suitability, and earth surface processes. This article presents a simple method for predicting soil depth by constructing a membership function based on fuzzy C-means. Materials and methods: This paper incorporates the soil type map, the land use map, and one type of DEM data to construct a soil-landscape model for soil depth prediction. It compares a fuzzy C-means classifier that includes expert judgment with a conditional autoregressive (CAR) model. Prediction efficiency was evaluated in the Three Gorges area of China using the root-mean-square error (RMSE) and the agreement coefficient (AC) of predictions at validation points. Results and discussion: The prediction stability of soil depth values from the fuzzy model is close to the regression model; the AC value indicates a better agreement by the fuzzy C-means method (0.428) than when using the regression model (0.420). The purposive sampling approach was provided by our method by the centroid where the fuzzy membership value is above 0.85, which improves the efficiency of the field sampling. The expansibility of our method is limited as the typical centroid sample location is dependent on the study area. The fuzzy membership value must be recalculated to provide a new typical centroid for field sample when enlarging the study area. Conclusions: The results indicate that the soil-landscape model constructed by the fuzzy membership value with fuzzy C-means method and the conventional soil map provides better quality soil depth spatial information on soil depth.
- Conditional autoregressive model
- Fuzzy C-means cluster
- Soil depth
- Soil-landscape model