Digital soil mapping (DSM) techniques have provided soil information that has revolutionized soil management across multiple spatial extents and scales. DSM practitioners have been increasingly reliant on machine-learning (ML) techniques; yet, methods to generate uncertainty maps from ML predictions are limited. To address this issue, this study integrates ML-based DSM with quantile regression (QR) in a methodological framework for estimating uncertainty. We test the proposed framework on two case study areas in Canada: (1) a dry-forest ecosystem in the Kamloops region of British Columbia, Canada; and (2) an agricultural system in the Ottawa region of Ontario, Canada. Four ML techniques (Random Forest, Cubist decision tree, k-nearest neighbors, and support vector machine) were compared using repeated cross-validation. Maps showing the 90% prediction interval (PI) were produced. Regardless of the case study, ML approach, and predicted soil variable, the uncertainty estimates were reliable and stable, according to the PI coverage probability analysis.

数字土壤制图 (DSM) 技术提供了土壤信息，彻底改变了跨多个空间范围和尺度的土壤管理。DSM 从业者越来越依赖机器学习 (ML) 技术；然而，从 ML 预测生成不确定性图的方法是有限的。为了解决这个问题，本研究将基于 ML 的 DSM 与分位数回归 (QR) 集成到估计不确定性的方法框架中。我们在加拿大的两个案例研究领域测试了提议的框架：(1) 加拿大不列颠哥伦比亚省坎卢普斯地区的干旱森林生态系统；(2) 加拿大安大略省渥太华地区的农业系统。四种 ML 技术（随机森林、立体派决策树、k-最近邻和支持向量机）使用重复交叉验证进行比较。产生了显示 90% 预测区间 (PI) 的地图。根据 PI 覆盖概率分析，无论案例研究、ML 方法和预测的土壤变量如何，不确定性估计都是可靠和稳定的。