Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R², the best predictive models were obtained for content of nitrogen (R² = 0.74), SOC (R² = 0.66), and potassium (R² = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

中文翻译：

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基于地形属性的俄罗斯跨农村草原区精准农业案例的土壤营养数字地图

领土的地形特征对土壤特性的空间分布有重大影响。这项研究的重点是主要农业化学土壤特性的数字土壤制图（DSM）-土壤有机碳（SOC），氮，钾，钙，镁，钠，磷，pH值和腐殖质累积（AB）的厚度跨乌拉尔草原地区（俄罗斯巴什科尔托斯坦共和国）的耕地面积。采用多元线性回归（MLR）和支持向量机（SVM）的方法预测土壤养分的空间分布和变化。我们使用了使用SRTM（航天飞机雷达地形任务）数字高程模型计算出的17个地形指数。结果表明，与MLR相比，支持向量机是预测所有土壤农用化学性质的空间变化的最佳方法。R ²是获得氮（R ² = 0.74），SOC（R ² = 0.66）和钾（R ² = 0.62）含量的最佳预测模型。在我们的研究中，海拔，坡度和MMRTF（多分辨率脊顶平坦度）指数是最重要的变量。所开发的方法可以用于研究相似景观中土壤养分的空间分布和大规模制图。