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Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon
Ye, Huichun1,2; Huang, Wenjiang1,2; Huang, Shanyu3; Huang, Yuanfang4; Zhang, Shiwen5; Dong, Yingying1; Chen, Pengfei6
2017-05-01
Source PublicationSPATIAL STATISTICS
ISSN2211-6753
Volume20Pages:76-91
Corresponding AuthorHuang, Wenjiang(huangwj@radi.ac.cn)
AbstractGeographically 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 GWRKMLRK>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.
KeywordSampling density Geographically weighted regression Geographically weighted regression kriging Soil organic carbon Spatial variation
DOI10.1016/j.spasta.2017.02.001
WOS KeywordSPATIAL PREDICTION ; REGIONAL-SCALE ; CHINA ; INTERPOLATION ; PRECIPITATION ; ATTRIBUTES ; SCHEMES ; MODELS ; MATTER ; STOCKS
Indexed BySCI
Language英语
Funding ProjectScience & Technology Basic Research Program of China[2014FY210100] ; National Natural Science Foundation of China[41501468] ; National Natural Science Foundation of China[41471186] ; Natural Science Foundation of Hainan Province, China[20154177] ; Natural Science Foundation of Hainan Province, China[2016CXTD015]
Funding OrganizationScience & Technology Basic Research Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Hainan Province, China
WOS Research AreaGeology ; Mathematics ; Remote Sensing
WOS SubjectGeosciences, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Remote Sensing ; Statistics & Probability
WOS IDWOS:000405608800004
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62731
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHuang, Wenjiang
Affiliation1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
2.Key Lab Earth Observat, Sanya 572029, Hainan, Peoples R China
3.Univ Cologne, Inst Geog, D-50923 Cologne, Germany
4.China Agr Univ, Coll Resources & Environm, Beijing 100193, Peoples R China
5.Anhui Univ Sci & Technol, Coll Earth & Environm, Huainan 232001, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Ye, Huichun,Huang, Wenjiang,Huang, Shanyu,et al. Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon[J]. SPATIAL STATISTICS,2017,20:76-91.
APA Ye, Huichun.,Huang, Wenjiang.,Huang, Shanyu.,Huang, Yuanfang.,Zhang, Shiwen.,...&Chen, Pengfei.(2017).Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon.SPATIAL STATISTICS,20,76-91.
MLA Ye, Huichun,et al."Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon".SPATIAL STATISTICS 20(2017):76-91.
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