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자료유형
학술저널
저자정보
저널정보
한국지질과학협의회 Geosciences Journal Geosciences Journal Vol.19 No.1
발행연도
2015.1
수록면
157 - 165 (9page)

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Regression analysis and kriging are popular spatialestimation methods often used in soil science to provide soil informationat different spatial resolutions and extent. Attempts havebeen made to combine them into a method known as regressionkriging (RK). With the increasing acceptance of digital soil mappingparadigm, utilization of spatial estimation method such as RK isbound to rise. Although RK is versatile and popular, its currentformat has deficiencies which can hinder the quality of estimatedsoil properties. One of the deficiencies of RK is the failure of itsregression model to recognize that natural soil occurs in groupswith unique response characteristics to soil forming factors. Ideally,these groups should be represented as a family of curves whenmodelling the landscape. However, the current applications tend touse average models which either block/control the grouping effectsor do not statistically recognize them. In this paper, mixed-effectsmodelling technique is shown for ingenious recognition of soil groupingsand consequent improvement of RK accuracy. Mixed-effectsmodelling allows for simultaneous regression estimation for individualmodels in a group and for different groups in the landscape. Its implementation in RK has been illustrated using executablescripts in R. It gives better mapping accuracy and reliable maps thanthe current application in RK. The new RK and its easy implementationin R software are anticipated to provide potential for wideapplication and eventual contribution to improved soil mappingand application of DSM.

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