Abstract. High resolution soil maps are urgently needed by land managers and researchers for a variety of applications. Digital Soil Mapping (DSM) allows to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. In this study, a legacy soil data set was used to train a machine learning algorithm in order to predict the particle size distribution within the catchment of the Bode river in Saxony-Anhalt (Germany). The ensemble learning method random forest was used to predict soil texture based on environmental covariates originating from a digital elevation model, land cover data and geologic maps. We studied the usefulness of clustering applications in addressing various aspects of the DSM procedure. To investigate the role of the imbalanced data problem in the learning process, the environmental variables were used to cluster the landscape of the study area. Different sampling strategies were used to create balanced training data and were evaluated on their ability to improve model performance. Clustering applications were also involved in feature selection and stratified cross-validation. Overall, clustering applications appear to be a versatile tool to be employed at various steps of the DSM procedure. Beyond their successful application, further application fields in DSM were identified. One of them is to find adequate means to include expert knowledge.

中文翻译：

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聚类方法在数字土壤制图中的优势：关于土壤质地分区的应用示例

摘要。土地管理人员和研究人员迫切需要高分辨率的土壤图，以用于多种应用。数字土壤测绘（DSM）可以通过经验模型将土壤特性与环境协变量相关联，从而对土壤特性进行分区。在这项研究中，使用了传统的土壤数据集来训练机器学习算法，以便预测萨克森-安哈尔特州（德国）博德河流域内的粒径分布。集合学习法随机森林用于基于源自数字高程模型，土地覆盖数据和地质图的环境协变量来预测土壤质地。我们研究了集群应用程序在解决DSM过程各个方面方面的有用性。为了研究数据不平衡问题在学习过程中的作用，使用环境变量对研究区域的景观进行聚类。使用了不同的采样策略来创建平衡的训练数据，并评估了它们改善模型性能的能力。聚类应用程序还参与了特征选择和分层交叉验证。总体而言，群集应用程序似乎是在DSM过程的各个步骤中都可以使用的通用工具。除了成功应用之外，还确定了DSM中的其他应用领域。其中之一是寻找适当的手段来包括专家知识。聚类应用程序还参与了特征选择和分层交叉验证。总体而言，群集应用程序似乎是在DSM过程的各个步骤中都可以使用的通用工具。除了成功应用之外，还确定了DSM中的其他应用领域。其中之一是寻找适当的手段来包括专家知识。聚类应用程序还参与了特征选择和分层交叉验证。总体而言，群集应用程序似乎是在DSM过程的各个步骤中都可以使用的通用工具。除了成功应用之外，还确定了DSM中的其他应用领域。其中之一是寻找适当的手段来包括专家知识。