Universität zu Köln

Ye, Huichun, Huang, Wenjiang, Huang, Shanyu, Huang, Yuanfang, Zhang, Shiwen, Dong, Yingying and Chen, Pengfei (2017). Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon. Spat. Stat., 20. S. 76 - 92. OXFORD: ELSEVIER SCI LTD. ISSN 2211-6753

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Abstract

Geographically weighted regression kriging (GWRK) is a popular interpolation method, considering not only spatial parametric nonstationarity and relationship between target and explanatory variables, but also spatial autocorrelation of residuals. However, little attention has been paid to the effects of different sampling densities on GWRK technique for estimating soil properties. Objectives of this study were: (i) comparing the GWRK predictions with those obtained from multiple linear regression kriging (MLRK) and ordinary kriging (OK), and (ii) examining how different sampling densities affect the performance of GWRK for predicting soil organic carbon (SOC). Soil samples were simulated with four sampling densities, including 0.010, 0.020, 0.041, and 0.082 sites/km(2). The results showed that GWRK made less prediction errors and outperformed MLRK and OK in the case of a high sampling density, with the root mean squared errors of GWRK<MLRK<OK and coefficient of determination of GWRK>MLRK>OK. However, in the case of a low sampling density, GWRK generated larger prediction errors, exhibiting a poorer performance than MLRK and OK. Accordingly, we conclude that GWRK can be considered as the best approach for predicting SOC in these three approaches with sufficient data points, but it has a poorer performance than the other methods with sparse data points. (C) 2017 Elsevier B.V. All rights reserved.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Ye, Huichun UNSPECIFIED UNSPECIFIED UNSPECIFIED Huang, Wenjiang UNSPECIFIED UNSPECIFIED UNSPECIFIED Huang, Shanyu UNSPECIFIED UNSPECIFIED UNSPECIFIED Huang, Yuanfang UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Shiwen UNSPECIFIED UNSPECIFIED UNSPECIFIED Dong, Yingying UNSPECIFIED UNSPECIFIED UNSPECIFIED Chen, Pengfei UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-231607
DOI:	10.1016/j.spasta.2017.02.001
Journal or Publication Title:	Spat. Stat.
Volume:	20
Page Range:	S. 76 - 92
Date:	2017
Publisher:	ELSEVIER SCI LTD
Place of Publication:	OXFORD
ISSN:	2211-6753
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SPATIAL PREDICTION; REGIONAL-SCALE; CHINA; INTERPOLATION; PRECIPITATION; ATTRIBUTES; VARIOGRAMS; SCHEMES; MODELS; MATTER Multiple languages Geosciences, Multidisciplinary; Mathematics, Interdisciplinary Applications; Remote Sensing; Statistics & Probability Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/23160