干旱区生态环境知识资源中心(Arid): Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China

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DOI	10.1016/j.ecolind.2019.02.038
	Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China
	Yang, Shun-Hua 1,2; Liu, Feng 1; Song, Xiao-Dong 1; Lu, Yuan-Yuan 1,2; Li, De-Cheng 1; Zhao, Yu-Guo 1,2; Zhang, Gan-Lin 1,2
通讯作者	Zhang, Gan-Lin
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2019
卷号	102 页码:252-264
英文摘要	Spatial prediction is an important approach to obtain location-specific values of soil electrical conductivity (EC), which is a proxy of soil salinity and important for agricultural management in arid and semi-arid areas. Linear regression models assume that the relation between soil EC and environmental covariates is constant over the area to be predicted. This is problematic at the regional scale, at which some of the regression parameters may indeed be globally constant, whereas others may vary locally. Moreover, model residuals often exhibit spatial dependence, which invalidates the ordinary least squares linear regression. This study examined the combination of a mixed geographically weighted regression model with simple kriging of the residuals (MGWGK) for mapping soil EC in the Heihe River Basin, an inland river basin in arid northwest China. We compared the performance of MGWRK with those of multiple linear regression (MLR), regression kriging (RK), geographically weighted regression (GWR), geographically weighted regression kriging (GWRK) and mixed geographically weighted regression (MGWR). Environmental covariates were developed from spatial information on topography, climate, vegetation, and geographic position. A ten-fold cross-validation was applied to evaluate predictive accuracy of the various methods. Soil EC ranged from 0.031 to 182.100 dS m(-1), exhibiting a contrasting distribution of soil EC in the upper, middle, and lower river reaches. The MGWRK method outperformed other methods. The effects of different environmental covariates on soil EC were revealed by the fixed and geographically varying parameters of MGWRK. The nugget-to-sill ratios of fitted variogram models all fell between 25 and 32%, exhibiting moderate spatial autocorrelation of models residuals. Predictive accuracy was improved by MGWRK, as the spatial dependence of model residuals were included in the prediction. When selecting an optimal linear regression model, covariates should be tested to see if they are constant (as in MLR) or spatially varying (as in GWR) or semi-varying (as in MGWR), and model residuals should be tested for spatial dependence (as in MGWRK).
英文关键词	Soil electrical conductivity Inland river basin Spatial non-stationarity Spatial dependence Mixed geographically weighted regression kriging
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000470964500025
WOS关键词	SOIL ORGANIC-CARBON ; SPATIAL PREDICTION ; REGIONAL-SCALE ; MATTER ; MODELS ; SENSITIVITY ; ATTRIBUTES ; VEGETATION ; ELEVATION ; VARIABLES
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/215208
作者单位	1.Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, 71 East Beijing Rd, Nanjing 210008, Jiangsu, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Yang, Shun-Hua,Liu, Feng,Song, Xiao-Dong,et al. Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China[J],2019,102:252-264.
APA	Yang, Shun-Hua.,Liu, Feng.,Song, Xiao-Dong.,Lu, Yuan-Yuan.,Li, De-Cheng.,...&Zhang, Gan-Lin.(2019).Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China.ECOLOGICAL INDICATORS,102,252-264.
MLA	Yang, Shun-Hua,et al."Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China".ECOLOGICAL INDICATORS 102(2019):252-264.