Understanding the spatial variation of soil properties is central to many sub-disciplines of soil science. Commonly in soil mapping studies, a soil map is constructed through prediction by a statistical or non-statistical model calibrated with measured values of the soil property and environmental covariates of which maps are available. In recent years, the field has gradually shifted attention towards more complex statistical and algorithmic tools from the field of machine learning. These models are particularly useful for their predictive capabilities and are often more accurate than classical models, but they lack interpretability and their functioning cannot be readily visualized. There is a need to understand how these models can be used for purposes other than making accurate prediction and whether it is possible to extract information on the relationships among variables found by the models. In this paper we describe and evaluate a set of methods for the interpretation of complex models of soil variation. An overview is presented of how model-independent methods can serve the purpose of interpreting and visualizing different aspects of the model. We illustrate the methods with the interpretation of two mapping models in a case study mapping topsoil organic carbon in France. We reveal the importance of each driver of soil variation, their interaction, as well as the functional form of the association between environmental covariate and the soil property. Interpretation is also conducted locally for an area and two spatial locations with distinct land use and climate. We show that in all cases important insights can be obtained, both into the overall model functioning and into the decision made by the model for a prediction at a location. This underpins the importance of going beyond accurate prediction in soil mapping studies. Interpretation of mapping models reveal how the predictions are made and can help us formulating hypotheses on the underlying soil processes and mechanisms driving soil variation.

了解土壤性质的空间变化是土壤科学许多子学科的核心。通常在土壤制图研究中，土壤图是通过统计或非统计模型的预测构建的，该模型使用土壤特性的测量值和可用的环境协变量进行校准。近年来，该领域逐渐将注意力从机器学习领域转移到更复杂的统计和算法工具上。这些模型对于它们的预测能力特别有用，并且通常比经典模型更准确，但它们缺乏可解释性并且它们的功能不能很容易地可视化。有必要了解这些模型如何用于进行准确预测之外的其他目的，以及是否有可能提取模型发现的变量之间关系的信息。在本文中，我们描述和评估了一套解释土壤变化复杂模型的方法。概述了独立于模型的方法如何服务于解释和可视化模型的不同方面的目的。我们在绘制法国表土有机碳的案例研究中解释了两种绘图模型的方法。我们揭示了土壤变异的每个驱动因素的重要性，它们的相互作用，以及环境协变量与土壤特性之间关联的函数形式。还针对具有不同土地利用和气候的区域和两个空间位置进行本地解释。我们表明，在所有情况下，都可以获得重要的见解，包括整体模型功能和模型为在某个位置进行预测而做出的决策。这强调了在土壤测绘研究中超越准确预测的重要性。绘图模型的解释揭示了预测是如何做出的，并且可以帮助我们对潜在的土壤过程和驱动土壤变化的机制提出假设。这强调了在土壤测绘研究中超越准确预测的重要性。绘图模型的解释揭示了预测是如何做出的，并且可以帮助我们对潜在的土壤过程和驱动土壤变化的机制提出假设。这强调了在土壤测绘研究中超越准确预测的重要性。绘图模型的解释揭示了预测是如何做出的，并且可以帮助我们对潜在的土壤过程和驱动土壤变化的机制提出假设。