The use of auxiliary geophysical data to improve a soil-landscape model

Soil-landscape models have prediction errors that can be reduced by using a uxiliary soil data. However, standard soil surveys using auger hole and lab oratory analysis encounter both methodological and economical constraints b ecause of, for example, the short-range variability of soils and the expens ive field work. In the present study, the objective was to test the use of auxiliary geophysical data to improve a soil-landscape model for two Digita l Elevation Model (DEM) resolutions (10 and 50 m), The study site was an ag ricultural parcel comprising an entire hillslope of a small (0.6 km(2)) cat chment in Normandy, France, Prediction models based on multiple regression and co-kriging techniques were established using topographic, soil and geop hysical data. For high DEM resolution (10 m), soil-landscape models based o nly on surface features seemed to be efficient, The use of additional geoph ysical or soil data improved the prediction quality slightly. For coarser D EM (50-m resolution), the prediction quality of models established using on ly terrain attributes was faulty, The soil hydromorphic prediction could be improved greatly by the use of auxiliary geophysical data. In this case, t he improved accuracy was similar to that obtained by high soil density data (50 observations/ha). Finally, we discuss the use of both geophysical and topographical data in order to describe better the spatial distribution of soils.