Spatially explicit prediction of soil aggregate stability based on machine learning approaches to identify soil quality degradation caused by soil erosion – ESTABLE –

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The project aims at the spatially differentiated prediction of the aggregate stability of the topsoil. As an indicator for the erosion susceptibility of uncovered or only temporarily covered soil surfaces, the distribution patterns of aggregate stability provide an indication of the degradation of soil structure associated with regular soil erosion. For the spatial representation of aggregate stability, the project develops regionalization models based on machine learning methods, which take into account not only the geomorphological area variables responsible for surface water and sediment transport and the physical and chemical soil properties which are decisive for the development of aggregate stability, but also different types of soil cultivation. The regionalization models that are to be developed are based on the learning methods such as multiple linear regression, regression kriging, decision tree (e.g. regression tree, boosted regression tree, boosted decision tree) and random forest methods that are widely used and tested in digital soil mapping. In order to test the suitability of the aggregate stability as an indicator for erosion-related soil structure changes and to statistically secure it on the basis of quantitatively comprehensible quantities, the regionalized aggregate stability is to be related to the soil erosion (net erosion quantities and rates) modelled for a period of 30 years with the erosion model EROSION-3D (E3D). The spatially different calculation of net erosion is based on all erosive precipitation events which occurred within the time period. The correlations between the spatial pattern of aggregate stability and the modelled long-term soil erosion finally serve to derive a regression model for the evaluation of the effects of long-term soil erosion and erosion intensities on the spatial distribution pattern of aggregate stability and for the spatially differentiated indication of soil erosion susceptibility.