Spatial representation of soil erodibility (Universal Soil Loss Equation's [USLE] K factor) is critical for soil conservation and erosion modeling. K factor is directly linked to the soil properties, which have a spatially continuous and soilscape related variability. The objective of this study was to test a methodology to map the spatial distribution of soil erodibility in a 1,200 ha sub‐basin making use of available spatial covariates and field data. The analysis was run for the Posses sub‐basin, in southeast Brazil. The topsoil erodibility was calculated at 85 sampled locations. The spatial prediction of soil erodibility was performed using the scorpan approach, in which the trend term for kriging with external drift (KED) was modeled by soilscape covariates selected by multiple linear regression analysis. The results confirmed that relief data could produce feasible results for digital soil erodibility mapping, especially when combined with geostatistical procedures. A comparison with ordinary kriging showed better error statistics and decreased variance of the estimates for the KED model. This could affect significantly the uncertainty of further USLE applications. The best agreement between KED erodibility values and direct measurements of the K factor was observed for the Red–Yellow Argisol (Red–Yellow Ultisol), which is the dominant soil class in the sub‐basin.

中文翻译：

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通过土壤景观趋势和克里金法进行数字土壤可蚀性制图

土壤易蚀性的空间表示（通用土壤流失方程的[USLE] K因子）对于土壤保护和侵蚀建模至关重要。K因子与土壤特性直接相关，而土壤特性具有空间连续性和与土壤景观有关的变异性。这项研究的目的是利用可用的空间协变量和田间数据，测试一种方法来绘制1200公顷子流域土壤易蚀性的空间分布图。分析是在巴西东南部的Posses子盆地进行的。在85个采样点计算了表土的可蚀性。土壤侵蚀性的空间预测是使用scorpan方法进行的，其中外部漂移克里金法（KED）的趋势项通过多元线性回归分析选择的土壤景观协变量建模。结果证实，救济数据可以为数字土壤可蚀性制图提供可行的结果，尤其是与地统计程序结合使用时。与普通克里金法的比较显示出更好的误差统计，并且减少了KED模型估计值的方差。这可能会严重影响进一步的USLE应用程序的不确定性。在亚流域主要的土壤类型为红色-黄色Argisol（红色-黄色Ultisol）时，观察到KED侵蚀度值与直接测量K因子之间的最佳一致性。这可能会严重影响进一步的USLE应用程序的不确定性。在亚流域主要的土壤类型为红色-黄色Argisol（红色-黄色Ultisol）时，观察到KED侵蚀度值与直接测量K因子之间的最佳一致性。这可能会严重影响进一步的USLE应用程序的不确定性。在亚流域主要的土壤类型为红色-黄色Argisol（红色-黄色Ultisol）时，观察到KED侵蚀度值与直接测量K因子之间的最佳一致性。