Map quality for ordinary kriging and inverse distance weighted interpolation
Map quality for ordinary kriging and inverse distance weighted interpolation
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DOI：
10.2136/sssaj2004.2042
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发表时间：
2004-11-01
影响因子：
2.9
通讯作者：
Shearer, SA
中科院分区：
文献类型：
--
作者：
Mueller, TG;Pusuluri, NB;Shearer, SA
空间插值方法的选择将影响土壤肥力图的质量。本研究的目的是描述和预测的相对性能的反距离加权（IDW）和普通克里格。在肯塔基州5个县的30.5米网格上采集土壤样品，并分析pH值、缓冲液pH值、P、K、Ca和Mg。从这些数据集，61米网格子集提取。数据插值IDW和克里格程序。使用独立数据集（PE验证）和交叉验证（PE交叉验证）确定预测效率（PE）。多元逐步回归被用来开发模型，描述了普通克里金和IDW的相对性能与数据的统计特性。在30.5米网格尺度下，普通克里金相对于IDW的性能随着空间相关性范围的增加和半变异函数模型拟合度的提高而提高。然而，在61.0米的网格尺度，普通克里金相对于IDW的性能下降的空间结构的程度增加，半变异函数模型的拟合提高。单独的PE交叉验证不能很好地描述PE验证在不同地点、土壤性质和采样间隔之间的表现（r（2）= 0.18）。然而，结合空间相关性的范围，在30.5米网格尺度下的显著变异性被描述为样本半变异函数达到平台期（R-2 = 0.61）的变量。在某些情况下，通过考虑空间相关性和交叉验证统计量的范围，可以更好地决定使用这些方法。
The selection of a spatial interpolation methods will impact the quality of site-specific soil fertility maps. The objective of this study was to describe and predict the relative performance of inverse distance weighted (IDW) and ordinary kriging. Soil samples were collected on 30.5-m grids for fields in five Kentucky counties and analyzed for pH, buffer pH, P, K, Ca, and Mg. From these data sets, 61-m grid subsets were extracted. Data were interpolated with IDW and kriging procedures. Prediction efficiency (PE) was determined using an independent dataset (PEvalidation) and with cross-validation (PEcross-validation). Multiple stepwise regression was used to develop models that described the relative performance of ordinary kriging and IDW with statistical properties of the data. At the 30.5-m grid scale, the performance of ordinary kriging relative to IDW improved as the range of spatial correlation increased and fit of the semivariogram model improved. However, at the 61.0-m grid scale, the performance of ordinary kriging relative to IDW diminished as the degree of spatial structure increased and the fit of the semivariogram model improved. Alone, PEcross-validation poorly describes the performance of PEvalidation across locations, soil properties, and sampling intervals (r(2) = 0.18). However, in combination with the range of spatial correlation, substantial variability at the 30.5-m grid scale was described for variables with sample semivariograms that reached plateaus (R-2 = 0.61). In some situations, better decisions will be made regarding the use of these methods by considering the range of spatial correlation and cross-validation statistics.