Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation. Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified. This restricts current applications of the maps in site-specific agricultural management and environmental modeling. We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation. The method consisted of three steps. First, environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors. Second, according to soil types of the pedon sites, the similarities were aggregated to derive similarity distribution for each soil type. Third, a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba, Ca nada. Based on 186 independent pedon sites, the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62. the map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map, which was commonly used in practice. Correct predictions mainly occurred in the agricultural plain area and the soil subgroups that are quite used in taxonomy, indicating that new environmental covariates need to be developed. We said that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.