Deep learning with a multi-task convolutional neural network to generate a national-scale 3D soil data product: Particle size distribution of the German agricultural soil-landscape

Abstract

Abstract. Many soil functions and processes are controlled by the soil particle size distribution. The generated three-dimensional continuous data product, which covers the particle size fractions of sand, silt, and clay in the agricultural soil-landscape of Germany, has a spatial resolution of 100 m and a depth resolution of 1 cm. This product is an important component for predicting the effects of agricultural management practices and their adaptability to climate change, as well as for analyzing soil functions and numerous risks. The effectiveness of the convolutional neural network (CNN) algorithm in producing multidimensional, multivariate data products is demonstrated. Even though the potential of this deep learning approach to understand and model the complex soil-landscape relationship is virtually limitless, limitations are data-driven. Further research is needed to assess the required complexity and depth of the CNN and the inclusion of the landscape surrounding each soil profile.