Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning

Digital soil mapping (DSM) techniques have provided soil information that has revolutionized soil management across multiple spatial extents and scales. DSM practitioners have been increasingly reliant on machine-learning (ML) techniques; yet, methods to generate uncertainty maps from ML predictions are limited. To address this issue, this study integrates ML-based DSM with quantile regression (QR) in a methodological framework for estimating uncertainty. We test the proposed framework on two case study areas in Canada: (1) a dry-forest ecosystem in the Kamloops region of British Columbia, Canada; and (2) an agricultural system in the Ottawa region of Ontario, Canada. Four ML techniques (Random Forest, Cubist decision tree, k-nearest neighbors, and support vector machine) were compared using repeated cross-validation. Maps showing the 90% prediction interval (PI) were produced. Regardless of the case study, ML approach, and predicted soil variable, the uncertainty estimates were reliable and stable, according to the PI coverage probability analysis.

Automated summary

This study integrates machine learning-based digital soil mapping with quantile regression to estimate uncertainty in soil predictions. Testing the framework in two case study areas in Canada showed that regardless of the machine learning approach or predicted soil variable, the uncertainty estimates were reliable and stable.