SFB 1070 ResourceCultures, University of Tübingen, 72074, Tübingen, Germany.
Cluster of Excellence Machine Learning: New Perspectives for Science, University of Tübingen, 72076, Tübingen, Germany.
Sci Rep. 2022 Jun 9;12(1):9496. doi: 10.1038/s41598-022-13514-5.
Multi-scale contextual modelling is an important toolset for environmental mapping. It accounts for spatial dependence by using covariates on multiple spatial scales and incorporates spatial context and structural dependence to environmental properties into machine learning models. For spatial soil modelling, three relevant scales or ranges of scale exist: quasi-local soil formation processes that are independent of the spatial context, short-range catenary processes, and long-range processes related to climate and large-scale terrain settings. Recent studies investigated the spatial dependence of topsoil properties only. We hypothesize that soil properties within a soil profile were formed due to specific interactions between different features and scales of the spatial context, and that there are depth gradients in spatial and structural dependencies. The results showed that for topsoil, features at small to intermediate scales do not increase model accuracy, whereas large scales increase model accuracy. In contrast, subsoil models benefit from all scales-small, intermediate, and large. Based on the differences in relevance, we conclude that the relevant ranges of scales do not only differ in the horizontal domain, but also in the vertical domain across the soil profile. This clearly demonstrates the impact of contextual spatial modelling on 3D soil mapping.
多尺度语境建模是环境制图的重要工具集。它通过在多个空间尺度上使用协变量来考虑空间依赖性，并将空间上下文和结构依赖性纳入机器学习模型中，从而对环境属性进行建模。对于空间土壤建模，存在三个相关的尺度或尺度范围：与空间背景无关的准局部土壤形成过程、短程连锁过程以及与气候和大尺度地形相关的长程过程。最近的研究仅调查了表土性质的空间依赖性。我们假设土壤剖面中的土壤性质是由于空间背景的不同特征和尺度之间的特定相互作用而形成的，并且存在空间和结构依赖性的深度梯度。结果表明，对于表土，小到中等尺度的特征不会提高模型精度，而大尺度则会提高模型精度。相比之下，底土模型受益于所有尺度——小、中、大。基于相关性的差异，我们得出结论，相关的尺度范围不仅在水平域中不同，而且在土壤剖面的垂直域中也不同。这清楚地表明了语境空间建模对 3D 土壤制图的影响。
