Does digital soil mapping prediction performance of soil texture improve when adding uncertain field texture estimates? A study based on clay content

• Laboratory and field texture weighted data for calibrating DSM of clay content. • Weighting calibration data strongly reduces prediction uncertainty compared to not weighting. • Adding field texture data using different sampling strategies changes maps and predictions uncertainties. • Different end-users may choose different strategies depending on the range of clay content values whose prediction interval they want to narrow. • Using a large number of hand-feel soil texture estimates leads to overly smooth maps and over-optimistic prediction intervals. Hand-feel soil texture observations (HFST) are less accurate, yet much more numerous, than laboratory measurements of soil texture (LAST). Therefore, it is tempting to incorporate both LAST and HFST information as calibration data in digital soil mapping (DSM) of particle-size distribution. We used about 1000 LASTs and 15,000 HFSTs over an area of about 6,800 km 2 . We incorporated the uncertainties of HFST and LAST calibration data in DSM and compared it with a case where measurement errors were ignored. We added progressively HFST calibration data to LAST data and ran predictions and k-fold validations keeping the same validation set for all experiments. We added HFSTs according to different strategies: either based on the most uncertain predicted areas from the LAST-only model, or those from the preceding LAST + nHFST model, or randomly. We discuss the pros and cons of these different strategies. Adding HFST data brought useful information for model calibration, but only if the uncertainty was accounted for. Various strategies for adding HFSTs led to different unbalanced samplings, maps and prediction intervals. We explain how these various unbalanced samplings sharpened or enlarged the predictive distribution of various clay content ranges. Adding a too large number of HFSTs led to an over-optimistic estimation of the 90 % prediction interval and to large homogeneous patterns, smoothing the spatial variation of clay content. Adding HFSTs using weights to acknowledge their uncertainty substantially improved DSM predictions, but the number of HFSTs and the strategy to add them must be carefully adapted.