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首页> 外文期刊>Geoderma: An International Journal of Soil Science >Intelligent estimation of spatially distributed soil physical properties.
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Intelligent estimation of spatially distributed soil physical properties.

机译：智能估算空间分布的土壤物理性质。

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Spatial analysis of soil samples is often times not possible when measurements are limited in number or clustered. To obviate potential problems, we propose a new approach based on the self-organizing map (SOM) technique. This approach exploits underlying nonlinear relation of the steady-state geomorphic concave-convex nature of hillslopes (from hilltop to bottom of the valley) to spatially limited soil textural data. The topographic features are extracted from Shuttle Radar Topographic Mission elevation data; whereas soil textural (clay, silt, and sand) and hydraulic data were collected in 29 spatially random locations (50 to 75 cm depth). In contrast to traditional principal component analysis, the SOM identifies relations among relief features, such as, slope, horizontal curvature and vertical curvature. Stochastic cross-validation indicates that the SOM is unbiased and provides a way to measure the magnitude of prediction uncertainty for all variables. The SOM cross-component plots of the soil texture reveals higher clay proportions at concave areas with convergent hydrological flux and lower proportions for convex areas with divergent flux. The sand ratio has an opposite pattern with higher values near the ridge and lower values near the valley. Silt has a trend similar to sand, although less pronounced. The relation between soil texture and concave-convex hillslope features reveals that subsurface weathering and transport is an important process that changed from loss-to-gain at the rectilinear hillslope point. These results illustrate that the SOM can be used to capture and predict nonlinear hillslope relations among relief, soil texture, and hydraulic conductivity data.
机译：当测量数量有限或成簇时，土壤样品的空间分析通常是不可能的。为了避免潜在的问题，我们提出了一种基于自组织映射（SOM）技术的新方法。这种方法利用了山坡（从山顶到谷底）的稳态地貌凹凸性质与空间有限的土壤质地数据之间的潜在非线性关系。从航天飞机雷达地形任务高程数据中提取地形特征;而在29个空间随机位置（50至75厘米深度）上收集了土壤质地（粘土，粉砂和沙子）和水力数据。与传统的主成分分析相比，SOM可以识别起伏特征之间的关系，例如坡度，水平曲率和垂直曲率。随机交叉验证表明SOM是无偏的，并提供了一种方法来测量所有变量的预测不确定性的大小。土壤质地的SOM跨分量图显示，在水文通量收敛的凹面地区，粘土比例较高，而在发散通量的凸面地区，比例较低。砂比具有相反的模式，在山脊附近较高，而在山谷附近较低。淤泥的趋势类似于沙子，尽管不太明显。土壤质地与凹凸山坡特征之间的关系表明，地下风化和运移是一个重要的过程，在直线山坡点处从损耗到增益变化。这些结果说明，SOM可用于捕获和预测地势，土壤质地和水力传导率数据之间的非线性坡度关系。

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