![]() ![]() Our study highlights the need for increased transparency in communication of uncertainty and limitations of derived map products. Whilst standard statistics evaluating the overall accuracy of the DSMs are highly significant, levels of accuracy across land use classifications vary considerably. ![]() Most crucially, we highlight the need for caution in relation to the assumed levels of accuracy of generated DSMs when considering only standard validation statistics, and the limitations of these approaches when data has bi-modal distribution, a common feature of data that encompasses both mineral and organic soils. In addition to accuracy assessment of each of the generated DSMs, we evaluate the suitability of each of these methods for DSM application. By allowing the models to select from identical input data we provide a fair comparison of each approach through isolating uncertainty in DSMs as a result of methodological choice. In this study, we quantify uncertainty in DSMs as a result of methodological choice we apply several approaches (Random forest, Gaussian Process, Generalised Additive Model, Neural Network and Linear Regression) to create multiple predictive models of SOC concentration across the UK. Much like with process-based models, there is a need to understand which data-science methodology is most suitable for a given research question and provide clarity on the magnitude of uncertainty associated with predictions. However, due to differences in both the range of data, methods and covariates used to drive predictive models, multiple DSMs created for the same areas are unlikely to be identical, which is indicative of the uncertainty associated with these mapped products. The predictive models often indicate impressively high levels of accuracy based on test/validation data. The model is used to extrapolate the prediction over the area for which covariate information is available. These maps are created by applying data-science methods to observational point data and associated covariates to create a predictive model. To meet this need Digital Soil Maps (DSMs) have gained significant provenance, providing high-resolution maps through spatial extrapolation of observed data to regional, national and global scales. ![]()
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