Soil landscape inference model (SoLIM) maps soil type, not soil mapping units, under the assumption that soil properties are fairly homogeneous over a small spatial extent. Thus, typically it takes a raster-based approach which means that it divides the area to be mapped into small pixels and determines the soil type for each pixel. Soil at each pixel is expressed in terms of its similarity to a set of prescribed soil types often referred to as ‘fuzzy soil mapping’. To avoid assigning a single soil type to a given location, similarity values (fuzzy membership values) are assigned to each soil type expressing the similarity of the local soil to each of the prescribed soil types. One must therefore know the types of soil existing in the area to be mapped. Soil types at a given location are predicted using the similarity values by assessing the environmental conditions at the location according to knowledge on how these conditions are related to the development of each soil type. Similarity values are generated by calculation the zonal statistics for each cluster in SAGA-GIS to obtain the range of values for each environmental covariate. The two key inputs to SoLIM are: data on the selected environmental covariates related to soil conditions in the area, and knowledge that describes the relationships between soils and the environmental variables.
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