Numerical soil classification using fuzzy K-means algorithm and predictive soil mapping at regional scale
基于K-均值算法模型的区域土壤数值化分类及预测制图

In the studied region, choosing texture, organic matter content, soil color, pH, electronic conductivity and soil layer thickness as soil variables, numerical soil classification based on 40 soil profiles was conducted by the use of fuzzy k-means algorithm, and the resulting central profiles soil 01, 18, 37, 38 and 40 were allocated into a hierarchical classification according to Key to Chinese Soil Taxonomy. They belonged to Pandian Series, Haplic Uap Ustic Cambisol, Yingju Series, Haplic Uap Ustic Cambisol, Haplic Endorusti-Ustic Cambisol, Haplic Warpic Anthric Entisol and Salinic Warpic Ustic Cambisol respectively. On the basis of the known taxonomic distances (Here,Euclidean distance was employed) amongst the sampled soils and the above central profiles, the taxonomic distance between unknown soils at any sites and the central profiles was figured out using Geostatistic techniques. In this way, continuous soil classification of the studied region was conducted through a predicted relationship of taxonomic distances between soils. For a better visualization, the soft borders between soils from different fuzzy classes were hardened by means of defuzzification defined through the distance thresholds. And the output of predictive soil mapping thus had a same visual appearance with a conventional soil map. From the predictive mapping, it could be clearly seen that the soil cover in the studied region was dominated by Haplic Endorusti-Ustic Cambisols and Salinic Warpic Ustic Cambisols, which accounts for a large percentage of the region's total area (46.3% and 33.2% respectively). By contraries, Haplic Uap Ustic Cambisols and Haplic Warpic Anthric Entisols were discontinuously distributed and totally occupied only around 10% of the total land area of the studied region. It could be concluded that integration of numerical soil classification and geostatistic interpolation would be one of the most recommendable approaches for predictive soil mapping.