Reducing location error of legacy soil profiles leads to significant improvement in digital soil mapping

Digital soil mapping relies on statistical relationships between soil profile observations and environmental covariates at the sample locations. However, inherent limitations of legacy soil profiles, such as inaccurate georeferencing and inconsistent sampling techniques, frequently introduce location errors into these soil profiles that greatly affect the quality of digital soil mapping. To address this challenge, this study focuses on reducing the location error of legacy soil profiles and evaluating the resulting impact on digital soil mapping. We enhanced the consistency between environmental covariates (i.e., elevation, slope and land use) with relative high accuracy and detailed descriptive information of legacy soil profiles to reduce the location error of legacy soil profiles. We constructed quantile regression forest models to predict soil properties and their uncertainty at different depths using soil profiles before and after location error correction. Our results demonstrate that for the majority of soil variables, correcting positional errors in legacy soil profiles significantly enhances the accuracy of the digital soil mapping. The largest improvement was found for soil organic carbon at 5 cm depth, with 21% increase of   R^2. The impact of reduced location error is particularly noteworthy in regions characterized by complex terrain or sparse sampling. In addition, the accuracy and details of the predicted maps are significantly improved, which better represent the spatial variation of soil attributes across China. Besides, we also found that elevation was the primary controlling factor for correcting location error of legacy soil profiles, followed by land use and slope. This research presents a significant step towards producing high-resolution and high-quality spatial soil datasets, which can provide essential support for soil management and ensure future soil security.