Terrain attributes are used as auxiliary variables in spatial prediction of soil classes and properties, due to their important role in the pedogenetic process and the increasing availability of digital elevation models (DEMs) in different resolutions. This work analyzed the effect of the different spatial resolutions of the DEMs and attributes derived from terrain and their implications for application in DSM predictive models. We used three spatial resolutions from different DEMs: (1) LIDAR – 2 m; (2) ALOS PALSAR – 12.5 and 30 m; (3) SRTM – 30 m; and (4) ASTER GDEM – 30 m. Multivariate analyses were performed determined by the Pearson linear correlation coefficient (r), the K-means cluster analysis, and the principal component analysis (PCA). The prediction of soil classes was performed using the terrain attributes grouped as to sensitivity to resolution, for different spatial resolutions, applying the machine learning algorithms Random Forest and Support Vector Machine. The cluster analysis indicated that most attributes remained within the same group of resolution sensitivity with changes in the cell size of reference DEM. The attributes of the terrain grouped low sensitive to resolution derived from the SRTM DEM showed better precision and the main advantage was the low cost and facilitating computational processing for application in the DSM.

地形属性在土壤分类和属性的空间预测中被用作辅助变量，这是因为它们在成岩过程中起着重要作用，并且在不同分辨率下数字高程模型（DEM）的可用性越来越高。这项工作分析了DEM的不同空间分辨率和源自地形的属性的影响及其在DSM预测模型中的应用意义。我们使用了来自不同DEM的三种空间分辨率：（1）LIDAR – 2 m；（2）ALOS PALSAR – 12.5和30 m；（3）SRTM – 30 m；（4）ASTER GDEM – 30 m。通过Pearson线性相关系数（r），K均值聚类分析和主成分分析（PCA）。使用机器学习算法随机森林和支持向量机，使用对分辨率敏感度分组的地形属性对土壤类别进行了预测，针对不同的空间分辨率。聚类分析表明，随着参考DEM像元大小的变化，大多数属性仍在同一组分辨率敏感度之内。从SRTM DEM导出的对分辨率低敏感的地形分组属性表现出更高的精度，主要优点是成本低廉，并且便于在DSM中进行计算处理。