3D soil parameter space of the agricultural landscape [Germany, Version 1]

Societal demands on soil functionality in agricultural soil-landscapes are confronted with yield losses and environmental impact. Soil functional information at national scale is required to address these challenges. Following the rationale that similarity in soils is reflected by similarity in landscape characteristics, soil functional types (SFTs) were defined and projected into space by machine learning. Each SFT is described by a multivariate soil parameter distribution along its depth profile. The agglomerated simplicity of the 3D multivariate soil parameter space into a limited number of spatially allocated process units provides the basis to run agricultural process models at national scale (Germany).
The data product refers to the parameter space until a depth of 1 m. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties: horizon occurrence probability of the organic horizon (symbol_H), the horizon with stagnic properties (symbol_S), groundwater influence (symbol G), and the C horizon (symbol_C), the depth limitation by bedrock (symbol_mC) as well as the soil properties texture (sand_content, silt_content, clay_content), stone content, and bulk density.
The data product consists of three files: A nationwide raster (SFT.tif) indicating the spatial allocation of the SFTs in terms of an identifier variable, and two related tables (MSPD1.csv and MSPD2.csv) including the information of the multivariate parameter distributions of the SFTs. The tables are linked to the raster by the identifier.
Please cite this data product by its DOI and the following reference:
Ließ M, Gebauer A, Don A (2021). Machine learning with GA optimization to model the agricultural soil-landscape of Germany: An approach involving soil functional types with their multivariate parameter distributions along the depth profile. Front. Environ. Sci. 9:692959. https://www.frontiersin.org/articles/10.3389/fenvs.2021.692959