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Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial
Mahmood Fazeli Sangani1, Davood Namdar Khojasteh2, Gary Owens3
1Department of Soil Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran. mfazeli@guilan.ac.ir.
Environmental Monitoring and Assessment
|October 30, 2019
Summary
Choosing the right spatial interpolation method is key for accurate soil salinity mapping. Ordinary kriging (OK) and inverse distance weighting (IDW) performed best, depending on field data characteristics.
Area of Science:
- Agricultural Science
- Soil Science
- Geostatistics
Background:
- Soil salinity is a critical factor affecting crop yield and agricultural sustainability.
- Accurate spatial mapping of soil electrical conductivity (EC) is essential for effective soil management.
- Different dataset characteristics and spatial dependencies exist across agricultural fields.
Purpose of the Study:
- To compare the performance of four spatial interpolation methods for mapping soil salinity (EC).
- To evaluate how dataset characteristics influence the accuracy of soil salinity prediction.
- To identify the optimal interpolation method for different agricultural field conditions.
Main Methods:
- Employed global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF).
- Assessed interpolation performance using mean bias error, root mean square error, mean absolute percentage error, and coefficient of determination.
- Analyzed dataset characteristics (central tendency, distribution) and spatial dependence strength via semivariogram analysis.
Main Results:
- Significant differences in dataset characteristics and spatial dependence were observed among the three agricultural fields.
- Ordinary kriging (OK) was the best method for fields A and C, while inverse distance weighting (IDW) was optimal for field B.
- Interpolation method performance was significantly influenced by field-specific data characteristics, often linked to management practices.
Conclusions:
- The optimal spatial interpolation method for soil salinity mapping varies depending on the specific characteristics of the agricultural field data.
- Accurate soil salinity mapping requires selecting an interpolation method that aligns with the collected data's statistical and spatial properties.
- Understanding field data characteristics is crucial for selecting appropriate geostatistical tools in precision agriculture.
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