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Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

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Abstract

Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model (DEM) and the Landsat Enhanced Thematic Mapper (ETM), respectively. These factors were contrasted for 334 soil samples (depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon (SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.

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Acknowledgements

The authors also acknowledge the financial support of Isfahan University of Technology (IUT) for this research. Moreover, we would like to thank Dr. Yeboah GYASI-AGYEI and the anonymous reviewers for their appreciated support, helpful suggestions and corrections.

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Authors and Affiliations

  1. Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Mojtaba Zeraatpisheh & Shamsollah Ayoubi

  2. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Kaifeng, 475004, China

    Mojtaba Zeraatpisheh

  3. Department of Soil Science, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, 6341773637, Iran

    Mojtaba Zeraatpisheh

  4. Department of Soil and Environment Sciences, Faculty of Agriculture, University of Khartoum, Shambat, 13314, Sudan

    Magboul Sulieman

  5. Instituto de Geomorfología y Suelos, Department of Geography, University of Málaga, Málaga, 29071, Spain

    Jesús Rodrigo-Comino

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  1. Mojtaba Zeraatpisheh
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  2. Shamsollah Ayoubi
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  4. Jesús Rodrigo-Comino
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Correspondence to Mojtaba Zeraatpisheh.

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Zeraatpisheh, M., Ayoubi, S., Sulieman, M. et al. Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran. J. Arid Land 11, 551–566 (2019). https://doi.org/10.1007/s40333-019-0059-9

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  • DOI: https://doi.org/10.1007/s40333-019-0059-9

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