Assessing and improving the transferability of current global spatial prediction models

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AbstractAimGlobal‐scale maps of the environment are an important source of information for researchers and decision makers. Often, these maps are created by training machine learning algorithms on field‐sampled reference data using remote sensing information as predictors. Since field samples are often sparse and clustered in geographic space, model prediction requires a transfer of the trained model to regions where no reference data are available. However, recent studies question the feasibility of predictions far beyond the location of training data.InnovationWe propose a novel workflow for spatial predictive mapping that leverages recent developments in this field and combines them in innovative ways with the aim of improved model transferability and performance assessment. We demonstrate, evaluate and discuss the workflow with data from recently published global environmental maps.Main conclusionsReducing predictors to those relevant for spatial prediction leads to an increase of model transferability and map accuracy without a decrease of prediction quality in areas with high sampling density. Still, reliable gap‐free global predictions were not possible, highlighting that global maps and their evaluation are hampered by limited availability of reference data.