Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping (DSM). The statistical or machine learning methods for selecting DSM covariates are not available for those situations with limited samples. To solve the problem, this paper proposed a case-based method which could formalize the covariate selection knowledge contained in practical DSM applications. The proposed method trained Random Forest (RF) classifiers with DSM cases extracted from the practical DSM applications and then used the trained classifiers to determine whether each one potential covariate should be used in a new DSM application. In this study, we took topographic covariates as examples of covariates and extracted 191 DSM cases from 56 peer-reviewed journal articles to evaluate the performance of the proposed case-based method by Leave-One-Out cross validation. Compared with a novices’ commonly-used way of selecting DSM covariates, the proposed case-based method improved more than 30% accuracy according to three quantitative evaluation indices (i.e., recall, precision, and F1-score). The proposed method could be also applied to selecting the proper set of covariates for other similar geographical modeling domains, such as landslide susceptibility mapping, and species distribution modeling.

选择合适的协变量集是影响数字土壤测绘（DSM）准确性的最重要因素之一。选择DSM协变量的统计或机器学习方法不适用于样本数量有限的情况。为了解决该问题，本文提出了一种基于案例的方法，可以将实际DSM应用程序中包含的协变量选择知识形式化。所提出的方法使用从实际DSM应用程序中提取的DSM案例对随机森林（RF）分类器进行了训练，然后使用经过训练的分类器来确定在新的DSM应用程序中是否应使用每个潜在的协变量。在这个研究中，我们以地形协变量为协变量的示例，并从56篇经同行评审的期刊文章中提取了191个DSM案例，以通过“留一法”交叉验证来评估所提出的基于案例的方法的性能。与新手常用的选择DSM协变量的方法相比，该建议的基于案例的方法根据三个定量评估指标（即，回忆，precision和F1得分）。所提出的方法还可以用于为其他类似的地理建模领域（例如滑坡敏感性图和物种分布建模）选择合适的协变量集。