Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

ABSTRACT: The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme; 2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property.

摘要：对未采样区域土壤理化性质（soil physical and chemical properties）的估算，可为土地管理、可持续发展及产水量评估提供关键参考信息。本研究以巴西两个代表不同地貌区域（physiographical regions）的典型流域为研究案例，采用基于知识的数字土壤制图（knowledge-based digital soil mapping）方法，对土壤理化性质进行建模与空间制图。研究选取了两组土壤-景观特征（soil-landscape features）迥异的流域，针对其土壤理化性质的空间建模与预测展开分析。由于该方法仅针对每种土壤类型采用单一土壤属性值，因此我们对典型值的选取方式，以及土地利用（land use）作为协变量（covariate）在预测中的作用进行了验证。本研究采用平均预测误差（mean prediction error, MPE）与均方根预测误差（root mean square prediction error, RMSPE）来评估预测方法的精度。基于模糊逻辑（fuzzy logics）的知识型数字土壤制图方法，在土壤属性空间预测中展现出良好的适用性，具体体现在：1）可采用低强度采样方案（sampling scheme）；2）适配巴西地区因资金有限难以开展高密度采样的现实场景；3）适用于呈非线性分布的属性，如饱和导水率（saturated hydraulic conductivity）。研究发现土地利用会对土壤属性的空间分布产生影响，因此将其纳入土壤建模与预测流程。针对不同情境下典型值的选取方式，不仅随预测方法的不同而有所差异，同时也与各土壤属性的空间分布特性息息相关。