Machine Learning (ML) algorithms are a promising alternative to traditional acquisition methods for creating new or updating existing soil maps. This study analyses the suitability of two ML techniques for the prediction of 36 different soil types in the Rur catchment in North-Rhine Westphalia (Germany). For this purpose, the performance of random forest (RF) and artificial neural network (ANN) classifiers have been investigated for three different scenarios with varying environmental co-variables for prediction and two varying training datasets with different sampling strategies. It has been analysed how the accuracy of classified digital soil map products is affected by the diversity of available soil types within different landscapes of the catchment, by varying topography, as well as different spatial resolutions of the co-variables and the distribution of training points. Co-variables derived from a digital elevation model (DEM) were once generated with a high-resolution DEM from airborne laser scanning data in a spatial resolution of 15 m and once with the 90 m TanDEM-X WorldDEM^tm. Results generally show best performance for the RF classification with overall accuracies (OA) over 70% with a spatially homogenized training dataset. The ANN classifier performed on average about 5% lower compared to RF. Furthermore, it could be shown for both algorithms that the OA is about 15% - 25% lower for areas in the northernmost and central part of the study area with a very diverse distribution of soil types, compared to other regions with only a few dominating soil types. Particularly for the ANN classifier with spatially homogenized training samples the observed drop in accuracy was considerably high for heterogeneous regions. A comparison of different predictor variables from different DEM sources with greatly varying spatial resolutions showed similar results for both datasets and an increase of accuracy with higher spatial resolutions could not be detected here. Overall, the classification accuracy is mainly affected by the sampling strategy of training samples, the diversity of distributed soil types and the availability of predictive environmental co-variables. In contrast, influence of topography and spatial resolution of DEM for the generation of predictor variables was only minor.

机器学习（ML）算法是用于创建新的或更新现有土壤图的传统采集方法的有希望的替代方法。这项研究分析了两种ML技术对北莱茵-威斯特法伦州（德国）Rur集水区中36种不同土壤类型的预测的适用性。为此，针对具有不同环境协变量进行预测的三个不同场景和具有不同采样策略的两个变化训练数据集，研究了随机森林（RF）和人工神经网络（ANN）分类器的性能。已经分析了流域不同景观中可用土壤类型的多样性，地形变化对分类数字土壤地图产品的准确性有何影响，以及协变量的不同空间分辨率和训练点的分布。从数字高程模型（DEM）导出的协变量是用高分辨率的DEM从机载激光扫描数据以15 m的空间分辨率生成的，然后是用90 m的TanDEM-X WorldDEM生成的。^Tm值。结果通常显示，在空间均质化训练数据集的基础上，RF分类的最佳性能具有70％以上的总体准确度（OA）。与RF相比，ANN分类器的性能平均降低了5％。此外，对于这两种算法，研究区域最北端和中部地区土壤类型的分布非常不同，与其他地区相比，OA降低了大约15％-25％土壤类型。特别是对于具有空间均一训练样本的ANN分类器，对于异类区域，观察到的准确性下降非常高。来自不同DEM来源的具有不同空间分辨率的不同预测变量的比较显示，两个数据集的结果相似，此处无法检测到具有较高空间分辨率的准确性的提高。总体而言，分类准确性主要受训练样本的采样策略，分布的土壤类型的多样性以及可预测的环境协变量的可用性的影响。相反，DEM的地形和空间分辨率对预测变量生成的影响很小。