Most of the Digital Soil Mapping (DSM) products now available across the globe have been developed from the deposits of punctual soil observations inherited from several decades of soil survey activity. By using these legacy data as inputs for calibrating our DSM models, we implicitly make the assumption that these legacy soil data are accurate and therefore do not affect significantly our DSM products. However, this assumption has never been tested. The objectives of this study were to evaluate the accuracies of soil property measurements retrieved from legacy soil profiles, to analyse the different sources of error that may affect these measurements and to examine their impacts on the soil property predictions delivered by DSM models. The study was focused on a control sampling within the coastal plain of Languedoc (Southern France) at 129 locations where legacy measured soil profiles were collected between 1955 and 1992. At each location, four topsoil (0–20 cm) samples were collected at increasing distances (0, 5, 25 and 100 m) to characterize the local variabilities of the soil properties. Six soil properties—Clay, Silt, Sand, Soil Organic Carbon, Calcium Carbonate contents and Cation Exchange Capacity—were determined for each sample using certified soil laboratory methods. The results revealed that legacy soil property values had large overall errors and large biases. Biases likely induced by differences in soil analysis protocols could be corrected by linear functions calibrated onto the reference data obtained from the control sampling. The contributions of the errors propagated from the manual geo‐referencing errors (mean = 31 m) represented on average 52% of the errors after analytical bias corrections. These errors exhibited large variations from one property to another due to differences in the short‐range spatial variations (0–100 m) of these soil properties. A DSM exercise conducted on our control sampling revealed that the errors of legacy soil data were propagated to the soil property predictions provided by the DSM models. However, this propagation could be largely mitigated by applying the above‐evoked corrections. This study highlights the need to better control the quality of the legacy soil data used in DSM and to account for this source of uncertainty in the DSM models.