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[1]WANG Shengli,LIU Wei,ZHANG Lianpeng,et al.Adaptive Surface Modeling of Soil Total Potassium Content Supported by the Environment Variables—A Case Study in Typical Areas of Qinghai Lake Basin[J].Research of Soil and Water Conservation,2018,25(01):132-138.

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Adaptive Surface Modeling of Soil Total Potassium Content Supported by the Environment Variables—A Case Study in Typical Areas of Qinghai Lake Basin

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 25 Number of periods: 2018 01 Page number: 132-138 Column: Public date: 2018-02-28

Title:: Adaptive Surface Modeling of Soil Total Potassium Content Supported by the Environment Variables—A Case Study in Typical Areas of Qinghai Lake Basin

Author(s):: WANG Shengli, LIU Wei, ZHANG Lianpeng, ZHAO Zhuowen, ZHU Shouhong; School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China

Keywords:: soil total potassium content; adaptive surface modeling; spatial interpolation; QingHai Lake Basin; environment variables

CLC:: TP79;G623.45

DOI:: -

Abstract:: The single global interpolation model is limited because of the complexity of spatial distribution of soil properties. The spatial discontinuity often causes a poor accuracy when the single model is used for surface modeling of soil properties faces in complex geomorphic area. This paper presented an adaptive method for surface modeling of soil properties supported by the environment variables (ASM-SP). Four methods, including Ordinary Kriging method (OK), Regression Kriging method (RK), Geographically Weighted Regression Kriging method (GWRK), Ordinary Co-Kriging method (OCK), were used to validate the proposed method. We selected the Qinghai Lake Basin, a typical complex geomorphic area, as the study area. Based on the 110 topsoil samples collected in 2013, the five methods were used to predict the spatial distribution of soil total potassium content, respectively. The mean error (ME), mean relative error (MRE), root mean square error (RMSE), accuracy (AC), correlation coefficient, regression coefficient and adjust coefficient were served as the evaluation indicators. The results show that:(1) the OK interpolation result is spatially smooth and has a weak bull’s-eye effect, it is an obvious deficiency in depicting spatial variability of soil total potassium content, and the accuracy also needs to be improved; (2) ASM-SP is the best method for predicting soil total potassium content with higher accuracy in this study. The result presents more details than other in the abrupt boundary, which can make the result consistent with the true geo-environmental variables. It not only considers the nonlinear relationship between geo-environment variables and soil properties, but also adaptively combines the advantages of multiple models. Compared with OK, RK, GWRK and OCK, the accuracy of ASM-SP increased by 9.27%, 6.29%, 2.66% and 7.74%, respectively. Therefore, the proposed method could provide significant reference for enriching the surface modelling theory and techniques.

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