Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km²) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape.

中文翻译：

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通过重采样分类树（DSMART）算法比较基于土壤图单元分解和协调的传统土壤图的三种空间分解方法

在数字土壤制图（DSM）领域，提高教育信息的空间分辨率是一项巨大的挑战。最初出现了几种分解常规土壤图的技术，并且它们的空间分辨率比解决环境和农业问题所需的分辨率更高。在区域一级，多边形地图将土壤覆盖表示为多边形的细分，从而定义了土壤地图单位（SMU），其中每个SMU可以包含一个或多个土壤类型单位（STU），并具有从专家知识中得出的给定比例。可以使用机器学习算法，通过“重采样分类树”（DSMART）对土壤图单元进行分解和协调，以更精细的空间分辨率对此类多边形图进行分解。本研究旨在比较基于DSMART决策树的传统土壤图空间分解的三种方法，以检验以下假设：土壤景观分布规则的分解可能会提高生成的土壤图的准确性。总体而言，测试了两种改进的DSMART算法（具有额外土壤剖面的DSMART；具有土壤景观关系的DSMART）和原始DSMART算法。在一个较大的研究区域（6775？km）上评估了分辨率为50m的分解土壤图的质量 测试了具有土壤景观关系的DSMART）和原始DSMART算法。在一个较大的研究区域（6775？km）上评估了分辨率为50m的分解土壤图的质量 测试了具有土壤景观关系的DSMART）和原始DSMART算法。在一个较大的研究区域（6775？km）上评估了分辨率为50m的分解土壤图的质量²）使用外部验证基于概率抽样选择的135个独立土壤剖面，755个遗留土壤剖面和现有详细的 1：25–000土壤图。还使用Shannon熵测度进行了成对比较，以在空间上定位分解后的地图之间的差异。主要结果表明，在分解过程中增加土壤景观关系可以增强土壤类型分布预测的性能。考虑到三个最可能的STU并使用135个独立的土壤剖面，DSMART的总体精度测度（预测与观测值相符的土壤剖面的百分比）为19.8％，而专家规则为18.1％。原始DSMART和DSMART的16.9％具有额外的土壤剖面。使用3×3进行验证时，这些措施几乎高出2倍视窗。他们获得了与土壤景观相关的DSMART的28.5％，原始DSMART和具有额外土壤观测的DSMART的25.3％和21％。通常，添加土壤景观关系和额外的土壤观测条件约束可以使模型预测在特定环境条件下可能发生的特定STU。因此，在DSMART算法中包括全球土壤景观专家规则对于获得一致的土壤图以及整个景观内SMU的清晰内部分解至关重要。