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Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

Author

Listed:
  • Magboul M. Sulieman

    (Department of Soil and Environment Sciences, Faculty of Agriculture, University of Khartoum, Shambat 13314, Sudan)

  • Fuat Kaya

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

  • Mohammed A. Elsheikh

    (Department of Soil and Environment Sciences, Faculty of Agriculture, University of Khartoum, Shambat 13314, Sudan)

  • Levent Başayiğit

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

  • Rosa Francaviglia

    (Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, 00184 Rome, Italy)

Abstract

A comprehensive understanding of soil salinity distribution in arid regions is essential for making informed decisions regarding agricultural suitability, water resource management, and land use planning. A methodology was developed to identify soil salinity in Sudan by utilizing optical and radar-based satellite data as well as variables obtained from digital elevation models that are known to indicate variations in soil salinity. The methodology includes the transfer of models to areas where similar conditions prevail. A geographically coordinated database was established, incorporating a variety of environmental variables based on Google Earth Engine (GEE) and Electrical Conductivity (EC) measurements from the saturation extract of soil samples collected at three different depths (0–30, 30–60, and 60–90 cm). Thereafter, Multinomial Logistic Regression (MNLR) and Gradient Boosting Algorithm (GBM), were utilized to spatially classify the salinity levels in the region. To determine the applicability of the model trained at the reference site to the target area, a Multivariate Environmental Similarity Surface (MESS) analysis was conducted. The producer’s accuracy, user’s accuracy, and Tau index parameters were used to evaluate the model’s accuracy, and spatial confusion indices were computed to assess uncertainty. At different soil depths, Tau index values for the reference area ranged from 0.38 to 0.77, whereas values for target area samples ranged from 0.66 to 0.88, decreasing as the depth increased. Clay normalized ratio (CLNR), Salinity Index 1, and SAR data were important variables in the modeling. It was found that the subsoils in the middle and northwest regions of both the reference and target areas had a higher salinity level compared to the topsoil. This study highlighted the effectiveness of model transfer as a means of identifying and evaluating the management of regions facing significant salinity-related challenges. This approach can be instrumental in identifying alternative areas suitable for agricultural activities at a regional level.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:9:p:1680-:d:1227060
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Ravinder Kumar & Pooja Dhansu & Neeraj Kulshreshtha & Mintu Ram Meena & Mahadevaswamy Huskur Kumaraswamy & Chinnaswamy Appunu & Manohar Lal Chhabra & Sstish Kumar Pandey, 2023. "Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. 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.
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