Maps of soil properties and classes are key inputs to land management, especially with the increasing awareness of ecosystem services provided by soils. Due to limited resources for new soil surveys, legacy soil inventories are often the major public source of soil data, and this is the case in Iran. One line of previous work has presented methods for data archaeology, rescue and renewal, and another line has shown the value of legacy surveys as covariates for digital soil mapping (DSM). The present study therefore aimed to integrate these two streams, adding another step of adequacy evaluation of the rescued surveys according to the Cornell guidelines. The study area is a 10,480 km² region located at the border of Isfahan and Chaharmahal-va-Bakhtiari provinces, Iran, covered by three legacy studies at the scale of 1: 50,000. The legacy maps were georeferenced and geocorrected, after which the Cornell adequacy guidelines were used to assess the quality of soil unit separation as evaluated at four levels of Soil Taxonomy. Evaluation was by weighted accuracy and the Tau coefficient, based on forty-one legacy soil profiles from a correlation study. The weighted accuracy of the map and the Tau index at all classification levels were respectively greater than 70% and 50%, which can be considered as satisfactory separation of soil units. Multinomial logistic regression (MLR) predictive models of soil classes with and without the legacy map as covariate were fit. The predictive accuracy of the models was improved at all taxonomic levels when the renewed legacy soil map was included as a covariate. We conclude that the selected legacy soil maps are of reasonable quality and can be used as reliable and useful inputs to DSM, and we propose that these procedures be widely applied.

土壤特性和类别的地图是土地管理的关键输入，特别是随着人们对土壤提供的生态系统服务的认识不断提高。由于用于新土壤调查的资源有限，遗留土壤清单通常是土壤数据的主要公共来源，伊朗就是这种情况。先前的一行研究提出了数据考古，救援和更新的方法，而另一条研究表明传统调查作为数字土壤制图（DSM）的协变量的价值。因此，本研究旨在整合这两个流，并根据康奈尔准则对获救调查进行了充分评估的另一步骤。研究区域是10,480 km ²该地区位于伊朗伊斯法罕省和恰哈马哈尔瓦-巴赫蒂阿里省的边界，由三个遗留研究覆盖，覆盖范围为1：50,000。对遗留地图进行地理参考和地理校正，然后使用康奈尔充足度准则评估土壤分类的质量（在四个土壤分类标准中进行了评估）。根据相关研究的41个传统土壤剖面，通过加权精度和Tau系数进行评估。在所有分类级别上，地图的加权准确度和Tau指数分别大于70％和50％，可以认为是令人满意的土壤单位分离。拟合带有和不带有遗留图作为协变量的土壤类别的多项式逻辑回归（MLR）预测模型。当将新的遗留土壤图作为协变量包括在内时，模型在所有分类学水平上的预测准确性均得到改善。我们得出的结论是，选定的遗留土壤图具有合理的质量，可以用作DSM的可靠且有用的输入，并且我们建议将这些程序广泛应用。