Mapping Soil Thickness by Integrating Fuzzy C-Means with Decision Tree Approaches in a Complex Landscape Environment

Predictive soil mapping depends on understanding the relationships between soil properties and environmental factors. However, in a complex soil landscapes, there is a shortage of suitable approaches to establish these relationships. The main objective is to predict soil thickness in an alpine watershed relating to soil environmental factors based on an unsupervised fuzzy clustering method (fuzzy c-means, FCM) and decision tree (DT) method. In this study, FCM method was used for stratifying the landscape, and then, a representative soil thickness was assigned to each class. For each class, a number of points were randomly chosen in proportion to representative areas, and then, the environmental factors at these point locations were extracted as a training data set (3626 points). For the training data set, DT method was used to obtain the critical threshold of soil–environment relationships. Finally, soil thickness map was created by applying the results of the DT across the region. An independently collected field sampling set (31 points) was used to evaluate the effectiveness of the proposed approach. For training set, 95.48 % of the total training data were correctly predicted. For validation set, the overall accuracy and Kappa coefficient could reach 74.2 % and 0.659, respectively. Evaluation accuracy of soil map was up to 74.2 %. In conclusion, it is suggested that soil–landscape modeling using FCM and DT methods can be efficiently used as a valuable research technique for spatial soil thickness prediction in a complex soil landscape where soil characteristics and properties are not available.