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Predictive Mapping of Electrical Conductivity and Assessment of Soil Salinity in a Western Türkiye Alluvial Plain

Author

Listed:
  • Fuat Kaya

    (Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Isparta University of Applied Sciences, Isparta 32260, Türkiye)

  • Calogero Schillaci

    (European Commission, Joint Research Centre, Via E. Fermi, 2749, 21027 Ispra, VA, Italy)

  • Ali Keshavarzi

    (Laboratory of Remote Sensing and GIS, Department of Soil Science, University of Tehran, P.O. Box 4111, Karaj 31587-77871, Iran)

  • Levent Başayiğit

    (Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Isparta University of Applied Sciences, Isparta 32260, Türkiye)

Abstract

The increase in soil salinity due to human-induced processes poses a severe threat to agriculture on a regional and global scale. Soil salinization caused by natural and anthropogenic factors is a vital environmental hazard, specifically in semi-arid and arid regions of the world. The detection and monitoring of salinity are critical to the sustainability of soil management. The current study compared the performance of machine learning models to produce spatial maps of electrical conductivity (EC) (as a proxy for salinity) in an alluvial irrigation plain. The current study area is located in the Isparta province (100 km 2 ), land cover is mainly irrigated, and the dominant soils are Inceptisols, Mollisols, and Vertisols. Digital soil mapping (DSM) methodology was used, referring to the increase in the digital representation of soil formation factors with today’s technological advances. Plant and soil-based indices produced from the Sentinel 2A satellite image, topographic indices derived from the digital elevation model (DEM), and CORINE land cover classes were used as predictors. The support vector regression (SVR) algorithm revealed the best relationships in the study area. Considering the estimates of different algorithms, according to the FAO salinity classification, a minimum of 12.36% and a maximum of 20.19% of the study area can be classified as slightly saline. The low spatial dependence between model residuals limited the success of hybrid methods. The land irrigated cover played a significant role in predicting the current level of EC.

Suggested Citation

  • Fuat Kaya & Calogero Schillaci & Ali Keshavarzi & Levent Başayiğit, 2022. "Predictive Mapping of Electrical Conductivity and Assessment of Soil Salinity in a Western Türkiye Alluvial Plain," Land, MDPI, vol. 11(12), pages 1-21, November.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2148-:d:986983
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    References listed on IDEAS

    as
    1. Amin I. Ismayilov & Amrakh I. Mamedov & Haruyuki Fujimaki & Atsushi Tsunekawa & Guy J. Levy, 2021. "Soil Salinity Type Effects on the Relationship between the Electrical Conductivity and Salt Content for 1:5 Soil-to-Water Extract," Sustainability, MDPI, vol. 13(6), pages 1-11, March.
    2. Nosetto, M.D. & Acosta, A.M. & Jayawickreme, D.H. & Ballesteros, S.I. & Jackson, R.B. & Jobbágy, E.G., 2013. "Land-use and topography shape soil and groundwater salinity in central Argentina," Agricultural Water Management, Elsevier, vol. 129(C), pages 120-129.
    3. Wadoux, Alexandre M.J.-C. & Heuvelink, Gerard B.M. & de Bruin, Sytze & Brus, Dick J., 2021. "Spatial cross-validation is not the right way to evaluate map accuracy," Ecological Modelling, Elsevier, vol. 457(C).
    4. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    5. Saskia Keesstra & Gerben Mol & Jan De Leeuw & Joop Okx & Co Molenaar & Margot De Cleen & Saskia Visser, 2018. "Soil-Related Sustainable Development Goals: Four Concepts to Make Land Degradation Neutrality and Restoration Work," Land, MDPI, vol. 7(4), pages 1-20, November.
    6. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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    Cited by:

    1. Magboul M. Sulieman & Fuat Kaya & Mohammed A. Elsheikh & Levent Başayiğit & Rosa Francaviglia, 2023. "Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions," Land, MDPI, vol. 12(9), pages 1-22, August.

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