title: Characterization of the spatial variability of soil available zinc at various sampling densities using grouped soil type information.
abstract: The influence of anthropogenic activities and natural processes involved high uncertainties to the spatial variation modeling of soil available zinc (AZn) in plain river network regions. Four datasets with different sampling densities were split over the Qiaocheng district of Bozhou City, China. The difference of AZn concentrations regarding soil types was analyzed by the principal component analysis (PCA). Since the stationarity was not indicated and effective ranges of four datasets were larger than the sampling extent (about 400 m), two investigation tools, namely F3 test and stationarity index (SI), were employed to test the local non-stationarity. Geographically weighted regression (GWR) technique was performed to describe the spatial heterogeneity of AZn concentrations under the non-stationarity assumption. GWR based on grouped soil type information (GWRG for short) was proposed so as to benefit the local modeling of soil AZn within each soil-landscape unit. For reference, the multiple linear regression (MLR) model, a global regression technique, was also employed and incorporated the same predictors as in the GWR models. Validation results based on 100 times realization demonstrated that GWRG outperformed MLR and can produce similar or better accuracy than the GWR approach. Nevertheless, GWRG can generate better soil maps than GWR for limit soil data. Two-sample t test of produced soil maps also confirmed significantly different means. Variogram analysis of the model residuals exhibited weak spatial correlation, rejecting the use of hybrid kriging techniques. As a heuristically statistical method, the GWRG was beneficial in this study and potentially for other soil properties.
authors.affiliation: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
authors.forename: Xiao-Dong
authors.initials: XD
authors.lastname: Song
authors.affiliation: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China. glzhang@issas.ac.cn.
authors.forename: Gan-Lin
authors.initials: GL
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
authors.forename: De-Cheng
authors.initials: DC
authors.lastname: Li
authors.affiliation: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
authors.forename: Yu-Guo
authors.initials: YG
authors.lastname: Zhao
mesh_terms: D002681:China; D004784:Environmental Monitoring; D016014:Linear Models; D015233:Models, Statistical; D012044:Regression Analysis; D045483:Rivers; D012987:Soil / Q000737:chemistry* / Q000145:classification; D062206:Spatial Analysis; D015032:Zinc / Q000032:analysis*
chemical_list: D012987:Soil; D015032:Zinc
keywords: Geographically weighted regression; Multiple linear regression; Non-stationarity; Principal component analysis; Soil type