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Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping

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Abstract

This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land.

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Abbreviations

EC:

electrical conductivity

GPI:

global polynomial interpolation

IDW:

inverse distance weighted

MAPE:

mean absolute percentage error

MBE:

mean bias error

OK:

ordinary kriging

R 2 :

coefficient of determination

RBF:

radial basis functions

RSS:

residual sum of squares

TNV:

mean total neutralizing values

κ:

Cohen’s kappa coefficient

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Acknowledgments

This research was supported by ETKA organization, Iran. The authors thank Mr. Hossein Mahmoudi for field and laboratory assistance. The authors are also grateful to the anonymous reviewers who considerably improved the quality of this manuscript prior to publication.

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Authors and Affiliations

  1. Department of Soil Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

    Mahmood Fazeli Sangani

  2. Department of Soil Science, Faculty of Agricultural Sciences, Shahed University, P.O.Box 18151-159, Tehran, Iran

    Davood Namdar Khojasteh

  3. Future Industries Institute, University of South Australia, Mawson Lakes, South Australia, 5095, Australia

    Gary Owens

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  1. Mahmood Fazeli Sangani
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Correspondence to Mahmood Fazeli Sangani.

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Fazeli Sangani, M., Namdar Khojasteh, D. & Owens, G. Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping. Environ Monit Assess 191, 684 (2019). https://doi.org/10.1007/s10661-019-7844-y

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