Disaggregating and updating a legacy soil map using DSMART, fuzzy c-means and k-means clustering algorithms in Central Iran

Abstract

Increasing demand for food production, global change, and growing population are the enormous challenges in recent decades. Accurate soil maps and adequate models are indispensable tools to assist managers, scientists, and decision-makers in addressing these challenges. Legacy soil polygon maps at national and regional scales are available widely, but lack detail, and therefore effective methods such as digital soil mapping (DSM) are needed to disaggregate these maps. The objective of this study was to disaggregate a legacy 1:1,000,000 soil map by three methods of disaggregation: a supervised classification method (DSMART algorithm) and two unsupervised classification methods including fuzzy c-means (FCM) and k-means (KM) clustering in Borujen region, Chaharmahal-Va-Bakhtiari Province, Central Iran for both great group and subgroup Taxonomic levels. Although field validation indicated that the accuracy of the disaggregated soil maps was lower than that of the conventional soil map at both levels of Soil Taxonomy, disaggregated approaches produced more detailed soil maps when compared with the first, second, and third most probable soil classes of the conventional soil map. The higher overall accuracy of the conventional soil map was due to soil association units which consist of more than one soil taxonomic class. FCM and DSMART methods produced more accurate and detailed disaggregated soil maps than KM clustering algorithm at the great group and subgroup levels, respectively. We concluded that the decision on what method to use depends on the map, the level of available information (map detail), available expert knowledge, and the availability of the soil unit composition percentages in the soil map legend.