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Rising temperatures and increasing climatic stress will push forest ecosystems in mountain regions towards their ecological limits, intensifying the need for informed decisions on forest management and tree species suitability. Such assessments rely on spatially explicit ecological models that require area-wide, depth-resolved soil information as a key input. Digital soil mapping (DSM) provides a framework to generate such information; however, the reliability and interpretability of model outputs strongly depend on how soil parent material information is represented at the input stage. Soil parent material, defined by bedrock and/or overlying material derived from geomorphological processes, is commonly described using geological maps and related datasets that encode properties as categorical units. While this representation is widespread across many landscapes, it becomes particularly limiting in complex mountain terrain, where fine-scale spatial variability and vertical heterogeneity arise from interacting geological and geomorphological processes. These processes strongly control the physical and chemical characteristics of the soil parent material but are difficult to resolve using class-based representations. In addition, soil parent material information is typically available at coarser and inconsistent spatial resolutions compared to terrain-derived predictors used in DSM. This limits its suitability for data-driven mapping and ecological modelling, where spatial consistency, depth discreteness and plausibility of predictors are essential. We address the preparation of soil parent material information as machine-learning-compatible predictors. Geological and geomorphological information differentiated by genetic process types is derived from project-specific geological and geomorphological field mapping and encoded as a set of categorical chemical and physical property classes, including rare but pedologically relevant types. These class-based descriptions are transformed into continuous representations of parent material composition, expressed by five mineral component layers and physical fractions describing grain-size distribution and proportions of consolidated (bedrock-derived) versus unconsolidated (deposit-derived) material. This preserves pedological meaning, reduces the dominance of unevenly represented classes in data-driven modelling, and enables proportional mixing across geometric soil depth intervals, resulting in more stable and interpretable learning than purely categorical predictors. Using a rule-based allocation scheme, polygon-based information on soil parent material genetics, layer thickness, and areal extent of unconsolidated cover is used to derive depth-discrete parent material layers for four geometric soil depth levels. Bedrock is represented as the basal parent material, while overlying unconsolidated material may be present with a defined areal coverage fraction within a homogeneous geological polygon. Vertical mixing is handled proportionally based on depth contribution. For parent material types associated with gravitational processes, an additional standalone two-dimensional distribution model, independent of mapped areal coverage information, is used to resolve pixel-scale presence or absence of overlying unconsolidated parent material. Parent material types associated with other genetic processes (e.g. aeolian, fluvial or glacial) are assumed to exhibit spatially continuous coverage and are mixed vertically according to their thickness. By providing vertically consistent and physically interpretable predictors, including the systematic transformation of class-based soil parent material descriptions into continuous representations, the proposed depth-aware approach enables the generation of area-wide, spatially coherent soil information. These products are suitable as input for downstream ecological applications, such as tree species suitability and soil hydrological assessments.