This paper introduces a model-agnostic approach designed to enhance uncertainty estimation in the predictive modeling of soil properties, a crucial factor for advancing pedometrics and the practice of digital soil mapping. For addressing the typical challenge of data scarcity in soil studies, we present an improved technique for uncertainty estimation. This method is based on the transformation of regression tasks into classification problems, which not only allows for the production of reliable uncertainty estimates but also enables the application of established machine learning algorithms with competitive performance that have not yet been utilized in pedometrics. Empirical results from datasets collected from two German agricultural fields showcase the practical application of the proposed methodology. Our results and findings suggest that the proposed approach has the potential to provide better uncertainty estimation than the models commonly used in pedometrics.

翻译：本文提出一种模型无关方法，旨在增强土壤特性预测建模中的不确定性估计，这是推进土壤计量学和数字土壤制图实践的关键因素。针对土壤研究中典型的数据稀缺挑战，我们提出一种改进的不确定性估计技术。该方法基于将回归任务转化为分类问题，不仅能够生成可靠的不确定性估计，还能应用尚未在土壤计量学中使用的、具有竞争性能的成熟机器学习算法。从德国两个农业田块采集的数据集获得的实证结果展示了所提方法的实际应用效果。我们的研究结果表明，与土壤计量学中常用的模型相比，所提方法有望提供更优的不确定性估计。