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Prediction of soil salinity in the Yellow River Delta using geographically weighted regression
Wu, Chunsheng1,2; Liu, Gaohuan1; Huang, Chong1
2017-06-01
Source PublicationARCHIVES OF AGRONOMY AND SOIL SCIENCE
ISSN0365-0340
Volume63Issue:7Pages:928-941
Corresponding AuthorHuang, Chong(huangch@lreis.ac.cn)
AbstractIt is essential to determine the content and spatial distribution of soil salinity in a timely manner because soil salinization can cause land degradation on a regional scale. Geographically weighted regression (GWR) is a local regression method that can achieve the spatial extension of dependent variables based on the relationships between the dependent variables and environment variables and the spatial distances between the sample points and predicted locations. This study aimed to explore the feasibility of GWR in predicting soil salinity because the existing interpolation methods for soil salinity in the Yellow River Delta are still of low precision. Additionally, multiple linear regressions, cokriging and regression kriging were added to compare the accuracy of GWRs. The results showed that GWR predicted soil salinity with high accuracy. Furthermore, the accuracy was improved when compared to other methods. The root mean square error, correlation coefficient, regression coefficient and adjustment coefficients between the observed values and predicted values of the validation points were 0.31, 0.65, 0.57 and 0.42, respectively, which were better than that of other methods, indicating that GWR is an optimal method.
KeywordSalinization local regression model environmental variables spatial interpolation Yellow River Delta
DOI10.1080/03650340.2016.1249475
WOS KeywordSPATIAL NON-STATIONARITY ; ORGANIC-MATTER ; GEOSTATISTICS ; VARIABILITY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41471335] ; National Natural Science Foundation of China[41271407] ; National Science - Technology Support Plan Projects[2013BAD05B03]
Funding OrganizationNational Natural Science Foundation of China ; National Science - Technology Support Plan Projects
WOS Research AreaAgriculture
WOS SubjectAgronomy ; Soil Science
WOS IDWOS:000399797600005
PublisherTAYLOR & FRANCIS LTD
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Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62626
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHuang, Chong
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Wu, Chunsheng,Liu, Gaohuan,Huang, Chong. Prediction of soil salinity in the Yellow River Delta using geographically weighted regression[J]. ARCHIVES OF AGRONOMY AND SOIL SCIENCE,2017,63(7):928-941.
APA Wu, Chunsheng,Liu, Gaohuan,&Huang, Chong.(2017).Prediction of soil salinity in the Yellow River Delta using geographically weighted regression.ARCHIVES OF AGRONOMY AND SOIL SCIENCE,63(7),928-941.
MLA Wu, Chunsheng,et al."Prediction of soil salinity in the Yellow River Delta using geographically weighted regression".ARCHIVES OF AGRONOMY AND SOIL SCIENCE 63.7(2017):928-941.
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