Machine learning for digital soil mapping: Applications, challenges and suggested solutions

Abstract The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Within the past two decades, soil scientists have applied ML to a wide range of scenarios, by mapping soil properties or classes with various ML algorithms, on spatial scale from the local to the global, and with depth. The wide adoption of ML for soil mapping was made possible by the increase in data availability, the ease of accessing environmental spatial data, and the development of software solutions aided by computational tools to analyse them. In this article, we review the current use of ML in DSM, identify the key challenges and suggest solutions from the existing literature. There is a growing interest in the use of ML in DSM. Most studies emphasize prediction and accuracy of the predicted maps for applications, such as baseline production of quantitative soil information. Few studies account for existing soil knowledge in the modelling process or quantify the uncertainty of the predicted maps. Further, we discuss the challenges related to the application of ML for soil mapping and suggest solutions from existing studies in the natural sciences. The challenges are: sampling, resampling, accounting for the spatial information, multivariate mapping, uncertainty analysis, validation, integration of pedological knowledge and interpretation of the models. Overall, the current literature shows few attempts in understanding the underlying soil structure or process using the predicted maps and the ML model, for example by generating hypotheses on mechanistic relationships among variables. In this regard, several additional challenging aspects need to be considered, such as the inclusion of pedological knowledge in the ML algorithm or the interpretability of the calibrated ML model. Tackling these challenges is critical for ML to gain credibility and scientific consistency in soil science. We conclude that for future developments, ML could incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to couple model prediction with pedological explanation and understanding of the underlying soil processes.