Key points are not available for this paper at this time.
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, could frequently introduce location errors into these soil profiles that 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 improved the agreement between the detailed descriptive information of legacy soil profiles and the relatively accurate environmental covariates (such as elevation, slope, and land use) to reduce the location errors of the legacy soil profiles. Quantile regression forest models were constructed to predict soil properties and their uncertainty using legacy 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 to some extent enhances the accuracy of the digital soil mapping. The largest improvement was found for soil organic carbon at 0–5 cm depth interval, with 21 % increase of MEC. The impact of reduced location error is particularly noteworthy in regions characterized by complex terrain. In addition, the details of the predicted maps of legacy soil profiles were improved after correcting for positional errors, which better represent the spatial variation of soil properties across China. Besides, we also found that elevation was the primary controlling factor for correcting location error of legacy soil profiles. This research presents a step towards producing high-resolution and high-quality spatial soil datasets, which can provide essential support for soil management and ensure future soil security.
Shi et al. (Wed,) studied this question.