Tropical Geography ›› 2020, Vol. 40 ›› Issue (5): 903-918.

### Comparative Study on Spatial Interpolation of Surface Soil Magnetic Properties in Guangzhou City

Chenjian He1(), Tingping Ouyang1,2(), Shasha Peng2

1. 1.College of Geography, South China Normal University, Guangzhou 510631, China
2.Key Laboratory of Ocean and Marginal Sea Geology, CAS, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
• Received:2019-12-30 Revised:2020-03-09 Online:2020-09-28 Published:2020-10-10
• Contact: Tingping Ouyang E-mail:245283134@qq.com;oyangtp@m.scnu.edu.cn

Abstract:

Soil magnetism is an important indicator of soil quality. Spatial distribution and environmental significance of surface soil magnetic properties have been reported from many areas worldwide. Spatial distribution of magnetic parameters was acquired through the spatial interpolation method and data of sampling sites was measured for a specific spatial scale in most of these studies. However, a limited number of studies in this field have appropriately reported on the accuracy of these spatial interpolation methods. Based on statistical characteristics analysis, normality test, and trend analysis, the results of 14 magnetic parameters of 350 surface soil samples collected from Guangzhou city were used to perform several spatial interpolation methods. Geostatistics (including Ordinary Kriging and Universal Kriging) and deterministic interpolation methods (including Inverse Distance Weighting and Radial Basis Function) were employed to conduct the spatial interpolation methods. The accuracy of spatial interpolation methods was measured by cross-validation. Mean Error, Root Mean Square Error, Mean Standardized Error, Root Mean Square Standardized Error and Average Standard Error were used to measure the accuracy of results of geostatistics methods; the optimal spatial interpolation method of each magnetic parameter of Ordinary Kriging and Universal kriging was selected. Next, Mean Error and Root Mean Square Error were used to measure the accuracy of results of deterministic interpolation methods; the optimal spatial interpolation method of each magnetic parameter of Inverse Distance Weighting and Radial Basis Function was selected. Finally, by comparing the Mean Error and Root Mean Square Error of the optimal deterministic interpolation method and geostatistical interpolation method, the optimal interpolation method of each magnetic parameter was determined. The results show that: First, among the geostatistical interpolation methods, the Ordinary Kriging method is effective for spatial interpolation of magnetic parameters χARM, χlf, χhf, S100, S300, S-100, SIRM, and HIRM; spatial interpolation of SIRM/χ should use the Universal Kriging method; Ordinary Kriging and Universal Kriging can be used for spatial interpolation of χARM/SIRM and S-300. Second, in the deterministic interpolation methods, the Radial Basis Function method is an optimal choice for spatial interpolation of χARM, χlf, χhf, χfd (%), χARM/χ, χARM/SIRM, and HIRM; for spatial interpolation of S100, S-100, and S-300, SIRM Inverse Distance Weighting method was the best alternative; Inverse Distance Weighting and Radial Basis Function methods could be used for spatial interpolation of χfd, SIRM/χ, and S300. Third, a comprehensive comparison of geostatistical interpolation methods and deterministic interpolation methods shows that spatial interpolation of χARM, χlf, χhf, χfd, χfd (%), χARM/χ, χARM/SIRM, S300, and HIRM should use the Radial Basis Function method; the Universal Kriging method is suitable for spatial interpolation of SIRM/χ. The Inverse Distance Weighting method should be chosen for spatial interpolation of S100, S-100, and S-300, spatial interpolation of SIRM should be measured with the Ordinary Kriging method.

CLC Number:

• P934