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Comparison of Sequential Indicator Simulation and Transition Probability Indicator Simulation Used to Model Clay Content in Microscale Surface Soil

机译:用于模拟微型表层土壤中黏土含量的顺序指标模拟和过渡概率指标模拟的比较

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Quantitative study of the spatial distribution of soil clay content is crucial to soil microecological research, soil physical and chemical properties, and agricultural and environmental management. In this article, the distribution of clay content within a 1-m(3) soil body was selected as the study object. The soil clay content was measured with a laser grain-size analyzer and classified into indicator data. The spatial variability of the data was then analyzed by indicator variogram and transiogram. The results of the indicator variogram showed that the spatial distribution of clay content in a horizontal direction is highly random. However, the results of the transiogram of clay content exhibited obvious spatial juxtapositional tendencies both vertically and horizontally. Subsequently, sequential indicator simulation (SIS) and transition probability indicator simulation (TPROGS) were applied to create conditional realizations of the 1-m(3) soil body. Finally, the realizations were validated by reproduction of a histogram, connectivity, as well as mean absolute error of prediction. The results indicated that the major textural classes were overestimated, whereas the minor classes were underestimated in the SIS-generated histogram, whereas all classes were well reproduced in the TPROGS. In addition, compared with the measured data, the connectivity of SIS realizations was significantly reduced, whereas the connectivity of TPROGS was coherent with measured data, which indicated that the crucial spatial characteristics, which were neglected by SIS, can be captured by TPROGS, even if the accuracy of prediction is similar. Therefore, the TPROGS method is a suitable method for characterizing the distribution of clay content in soil. The results may provide useful information for soil research.
机译:土壤粘土含量空间分布的定量研究对于土壤微生态研究,土壤理化性质以及农业和环境管理至关重要。在本文中,选择1-m(3)土体内的粘土含量分布作为研究对象。用激光粒度分析仪测量土壤粘土含量,并分类为指示剂数据。然后通过指示剂变异函数和transiogram分析数据的空间变异性。指示变量图的结果表明,水平方向上粘土含量的空间分布是高度随机的。然而,粘土含量的透射图的结果在垂直和水平方向上都表现出明显的空间并置趋势。随后,顺序指标模拟(SIS)和过渡概率指标模拟(TPROGS)被用于创建1-m(3)土体的条件实现。最后,通过再现直方图,连接性以及预测的平均绝对误差来验证这些实现。结果表明,在SIS生成的直方图中,主要的纹理类别被高估了,而次要的类别被低估了,而TPROGS中的所有类别都得到了很好的再现。此外,与实测数据相比,SIS实现的连通性显着降低,而TPROGS的连通性与实测数据是一致的,这表明SIS忽略的关键空间特征甚至可以被TPROGS捕获。如果预测的准确性相似。因此,TPROGS方法是表征土壤中粘土含量分布的合适方法。研究结果可为土壤研究提供有用的信息。

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