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Selection of optimal auxiliary soil nutrient variables for Cokriging interpolation
Genxin Song1, Jing Zhang1, Ke Wang1
1Institute of Agricultural Remote Sensing and Information Technique, Zhejiang University, Hangzhou, Zhejiang, China; and Ministry of Education Key Laboratory of Environmental Remediation, Ecological and Health, Zhejiang University, Hangzhou, Zhejiang, China.
Plos One
|June 14, 2014
Summary
Selecting the best auxiliary variables (BAVs) significantly improves Cokriging interpolation accuracy for soil nutrients. This study demonstrates that using highly correlated variables enhances soil attribute mapping precision.
Area of Science:
- Geosciences
- Soil Science
- Spatial Statistics
Background:
- Accurate soil attribute interpolation is crucial for precision agriculture and environmental management.
- The Cokriging method offers a powerful tool for spatial interpolation by incorporating auxiliary variables.
- Selecting optimal auxiliary variables is key to maximizing the efficiency of Cokriging.
Purpose of the Study:
- To investigate the selection of best auxiliary variables (BAVs) for Cokriging interpolation of soil nutrients.
- To compare the interpolation accuracy of Cokriging using BAVs versus poor auxiliary variables (PAVs).
Main Methods:
- Collected 670 soil samples to determine nutrient and trace element attributes.
- Utilized Digital Elevation Model (DEM) data to analyze spatial autocorrelation and coordinate relationships.
- Selected BAVs based on high correlation with target soil nutrient attributes (organic matter, total N, available P, available K).
Main Results:
- Cokriging interpolation using BAVs demonstrated significantly higher accuracy compared to using PAVs.
- Mean absolute errors for BAV interpolation were substantially lower across all tested soil nutrients.
- BAV selection led to improved accuracy in interpolating organic matter, total N, available P, and available K.
Conclusions:
- The selection of BAVs is critical for enhancing the accuracy of Cokriging interpolation for soil nutrients.
- This research provides valuable insights for selecting appropriate auxiliary parameters in Cokriging applications.
- Effective BAV selection optimizes spatial prediction of soil properties, supporting better land management decisions.
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