Prediction of soil properties by digital terrain modelling
We investigated two approaches for large-scale analysis and prediction of the spatial distribution of soil properties in an agricultural landscape in the Canadian prairies. The first approach was based on the implementation of nine types of digital terrain models (DTMs) and regression analysis of soil and topographic data. The second approach used a concept of accumulation, transit, and dissipation zones of the landsurface. Soil properties were soil moisture, residual phosphorus, solum thickness, depth to calcium carbonate, and organic carbon content. The dependence of soil properties on topography was supported by correlations for the upper soil layer. However, topographic control of soil moisture and residual phosphorus decreased with depth. Also, correlation coefficients and regression equations describing topographic control of soil moisture and residual phosphorus differed among seasons. This imposes limitations on regression-based predictions of the spatial distribution of soil properties. The prediction of soil property distribution with the concept of accumulation, transit and dissipation zones can be more successful and appropriate than the prediction based on linear regression. The variability in relationships between soil and topographic characteristics with depth may stem from spatial variability in the rate of decline of hydraulic conductivity with depth. Temporal variability in soiltopography relationships occurs because soil properties result from interactions of a variety of pedogenetic factors and processes marked by different temporal variability. In soil studies with digital terrain modelling, there is a need to take into account four types of variability in relations between soil and relief: regional, temporal, depth, and scale.
soil digital terrain model topography prediction map statistical analysis