Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information?
Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information?
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DOI：
10.1016/j.geoderma.2010.06.017
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发表时间：
2010-10-15
期刊：
影响因子：
6.1
通讯作者：
Li, Yong
中科院分区：
文献类型：
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作者：
Li, Yong
通过结合地形（TOPO）和遥感（RS）数据的二次信息，可以提高土壤有机质（SOM）含量等土壤性质数据的质量，并降低空间采样强度。本研究采用Hengl et al.（2004）开发的回归克里格（RK）空间插值的通用框架，利用内部次要变量（采样坐标）和外部辅助信息，如土壤图（soil）、Landsat 5 TM图像的植被指数（VIs）以及多个地形属性（高程、坡度、收敛度和湿度指数、平面和剖面曲率），评估RK改进SOM空间插值的能力。同时，采用普通克里格法（OK）和通用克里格法（UK）对SOM的空间分布进行插值比较。本研究结果表明，在回归模型中加入更多的辅助信息后，RK对SOM的预测精度没有提高，而在TOPO、VI和SOIL信息组合时，特别是最后一个信息，RK对SOM的预测精度显著下降。当最小采样距离从25 m增加到500 m，或采样密度从0.42降低到0.26 # km（-2）时，RK技术在提高粗采样分辨率下的SOM预测精度方面并不优于OK和UK。有趣的是，在最小采样距离为250 m时，所有插值方法的SOM预测精度最高。因此，从最小采样距离、采样密度、目标变量空间分辨率与辅助信息或空间尺度的兼容性等方面讨论了RK实现在空间插值中的适用性。(C) 2010 Elsevier B.V.版权所有
The data quality of soil properties, such as the soil organic matter (SOM) content, can be improved and the spatial sampling intensities may be reduced by incorporating secondary information, such as those derived from topographic (TOPO) and remote sensing (RS) data to enhance their spatial estimates. This study adopted a generic framework for spatial interpolation using regression kriging (RK) developed by Hengl et al. (2004) to evaluate RK's capability in improving SOM spatial interpolation using internal secondary variables (sampling coordinates) and external auxiliary information, such as soil map (SOIL), vegetation indices (VIs) derived from a Landsat 5 TM image, and several terrain attributes (elevation, slope, convergence and wetness indices, and plan and profile curvatures). Meanwhile, the SOM spatial distribution was also interpolated by using ordinary kriging (OK) and universal kriging (UK) methods for comparison purposes. The results of this study showed that the prediction accuracy of SOM by using RK was unimproved with the inclusion of more auxiliary information in the regression models, but in contrast it significantly declined when TOPO, VI and SOIL information were combined, particularly the last one. It was also observed that with the increase of the minimum sampling distances from 25 to 500 m or with the decrease of the sampling densities from 0.42 to 0.26 # km(-2), the RK techniques did not outperform OK and UK in improving the SOM prediction accuracy at coarse sampling resolutions. Interestingly, the highest accuracy of the SOM prediction by all these interpolation methods was achieved at the minimum sampling distance of 250 m. The suitability of RK implementation in the spatial interpolation was therefore discussed by considering the minimum sampling distance, the sampling density and the compatibility of spatial resolutions of target variables and auxiliary information or the spatial scales. (C) 2010 Elsevier B.V. All rights reserved.