Using bias correction and ensemble modelling for predictive mapping and related uncertainty: A case study in digital soil mapping

Abstract

In this study, we explored the potential benefits of using bias correction and ensemble modelling for the prediction of soil properties and assessment of related uncertainty. The proposed approach combines resampling techniques applied to soil observations, covariates and hyperparameters to generate a set of simulated values at the same location. The ensemble predictions resulting from the resampling are then used to generate deterministic predictions for the final mapping product along with the related uncertainty. We also introduced bias correction into the modelling framework in order to overcome conditional bias that is commonly encountered in digital soil mapping products. We compared the accuracy of our predictions resulting from bias correction and ensemble modelling with previously published global soil mapping products. Our results demonstrated that bias correction improves the linearity and the ratio of the variance between simulated and observed values and reduces conditional bias by a factor of 25 to 50% for different soil properties. The performance of the deterministic predictions obtained from ensemble modelling is better than most of its individual component models, and is always located in the first quantile of the performance of all members. The analysis of uncertainty suffers from underdispersion, which means that local uncertainty tends to be underestimated by our approach 40 to 60% of the time. A comparison with the performance achieved by global soil mapping products in our area, indicates that global mapping products achieved low performance (R2: −0.48–0.13) and suffered from an important conditional bias (alpha: 0.23–0.59, where alpha is the ratio of variance between predicted and observed values), leading to unrealistic predictions at the local scale. Ultimately, the combination of bias correction and ensemble modelling appears to be both useful and relevant for digital soil mapping and helps to address three common problems: equifinality, assessment of uncertainty and, correction of conditional bias in simulated values. The procedure described in this study is relatively easy to implement and is not computationally intensive. In operational use, the combination of bias correction and ensemble modelling should increase the quality of the information produced for environmental management and modelling, while additionally providing uncertainty maps.