Digital Soil Mapping (DSM) is increasingly needed to improve existing soil information and derive soil property maps at the suitable spatial resolution for sustainable soil landscape management. However, predicting several soil properties while preserving specific pedological process is a great challenge, particularly when only coarse soil information is available over large areas. Spatial disaggregation seems to be an effective technique to extract pedological information by downscaling the original information to produce soil maps at finer resolutions. In a previous study, legacy soil maps of Brittany (France) were disaggregated at a 50 m spatial resolution using the DSMART (Disaggregation and Harmonization of Soil Map Units Through Resampled Classification Trees) algorithm and pedological knowledge. The present study had two main objectives: (i) assess the preservation of the relationships between soil properties when soil properties are estimated at standard depths by applying the equal-area spline method on soil data at pedon scale, and (ii) combine disaggregated soil maps and spline-function results to estimate spatial patterns of nine soil properties for six regular soil-depth intervals down to 200 cm across Brittany, an area of 27,040 km². To this end, soil properties were first generated for standard soil-depth intervals using spline functions. Then, for mapping soil properties at the six standard depths, weighted mean of each soil attribute was calculated for each grid cell from reference soil-property values of the three most probable predicted soil types. Their associated probabilities of occurrence were used as weights. To assess the ability of spline functions to preserve soil-property relationships, multiple statistical analyses were performed using original and splined soil datasets. Bivariate and multivariate analysis highlighted that spline functions preserved soil-property relationships. Derived digital soil maps showed strong spatial patterns: SOC and silt contents generally decreased with depth, while sand content and coarse fragment percentage consistently increased with depth. In addition, experimental semivariogram analysis of SOC content showed high spatial variability over short distances for all soil-depth intervals except the deepest (100–200 cm), while silt content showed high semivariance for the deepest soil layers. This study can be considered an example of harmonization to common output specifications, which generates a geo-database of quantitative soil properties that describe lateral and vertical soil variation for regular depth intervals. These predictions can be incorporated into environmental models to help decision makers manage landscapes.

越来越需要数字土壤制图（DSM），以改善现有的土壤信息并以合适的空间分辨率导出土壤特性图，以进行可持续的土壤景观管理。但是，在保留特定的土壤学过程的同时预测几种土壤特性是一个巨大的挑战，特别是在大面积地区只有粗土信息的情况下。空间分解似乎是一种有效的技术，它可以通过按比例缩小原始信息的规模来提取土壤学信息，从而以更高分辨率生成土壤图。在先前的研究中，使用DSMART（通过重采样分类树对土壤图单元进行分解和协调）算法和教育学知识，以50 m的空间分辨率对布列塔尼（法国）的遗留土壤图进行了分解。本研究有两个主要目标：²。为此，首先使用样条函数针对标准土壤深度间隔生成土壤属性。然后，为绘制六个标准深度的土壤属性，从三种最可能的预测土壤类型的参考土壤属性值为每个网格单元计算每个土壤属性的加权平均值。它们的相关发生概率用作权重。为了评估样条函数保持土壤属性关系的能力，使用原始和样条土壤数据集进行了多次统计分析。双变量和多变量分析强调，样条函数保留了土壤与土壤之间的关系。衍生的数字土壤图显示出很强的空间格局：SOC和淤泥含量通常随深度而降低，含沙量和粗碎屑百分比随深度而不断增加。此外，对土壤有机碳含量进行的实验半方差分析表明，除最深（100-200 cm）以外，所有土壤深度区间的短距离土壤空间变异性均较高，而最深土壤层的粉沙含量均具有较高的半变异性。可以认为该研究是协调通用输出规范的示例，该规范生成了定量土壤属性的地理数据库，该数据库描述了规则深度间隔的横向和垂直土壤变化。这些预测可以纳入环境模型中，以帮助决策者管理景观。SOC含量的实验半变异函数分析显示，除最深（100-200 cm）以外，所有土壤深度区间的短距离内空间变异性均较高，而最深土壤层的粉沙含量均具有较高的半变异性。可以认为该研究是协调通用输出规范的示例，该规范生成了定量土壤属性的地理数据库，该数据库描述了规则深度间隔的横向和垂直土壤变化。这些预测可以纳入环境模型中，以帮助决策者管理景观。SOC含量的实验半变异函数分析显示，除最深（100-200 cm）以外，所有土壤深度区间的短距离内空间变异性均较高，而最深土壤层的粉沙含量均具有较高的半变异性。可以认为该研究是协调通用输出规范的示例，该规范生成了定量土壤属性的地理数据库，该数据库描述了规则深度间隔的横向和垂直土壤变化。这些预测可以纳入环境模型中，以帮助决策者管理景观。它会生成一个定量土壤属性的地理数据库，该数据库描述了规则深度间隔的横向和垂直土壤变化。这些预测可以纳入环境模型中，以帮助决策者管理景观。它会生成一个定量土壤属性的地理数据库，该数据库描述了规则深度间隔的横向和垂直土壤变化。这些预测可以纳入环境模型中，以帮助决策者管理景观。