Using deep learning for multivariate mapping of soil with quantified uncertainty

Abstract

Digital soil mapping (DSM) techniques are widely employed to generate soil maps. Soil properties are typically predicted individually, while ignoring the interrelation between them. Models for predicting multiple properties exist, but they are computationally demanding and often fail to provide accurate description of the associated uncertainty. In this paper a convolutional neural network (CNN) model is described to predict several soil properties with quantified uncertainty. CNN has the advantage that it incorporates spatial contextual information of environmental covariates surrounding an observation. A single CNN model can be trained to predict multiple soil properties simultaneously. I further propose a two-step approach to estimate the uncertainty of the prediction for mapping using a neural network model. The methodology is tested mapping six soil properties on the French metropolitan territory using measurements from the LUCAS dataset and a large set of environmental covariates portraying the factors of soil formation. Results indicate that the multivariate CNN model produces accurate maps as shown by the coefficient of determination and concordance correlation coefficient, compared to a conventional machine learning technique. For this country extent mapping, the maps predicted by CNN have a detailed pattern with significant spatial variation. Evaluation of the uncertainty maps using the median of the standardized squared prediction error and accuracy plots suggests that the uncertainty was accurately quantified, albeit slightly underestimated. The tests conducted using different window size of input covariates to predict the soil properties indicate that CNN benefits from using local contextual information in a radius of 4.5 km. I conclude that CNN is an effective model to predict several soil properties and that the associated uncertainty can be accurately quantified with the proposed approach.