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A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China

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  • Jinlin Li

    (Key Laboratory of West China’s Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Center for Dryland Water Resources Research and Watershed Science, Lanzhou University, Lanzhou 730000, China)

  • Lanhui Zhang

    (Key Laboratory of West China’s Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Center for Dryland Water Resources Research and Watershed Science, Lanzhou University, Lanzhou 730000, China)

  • Chansheng He

    (Key Laboratory of West China’s Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Center for Dryland Water Resources Research and Watershed Science, Lanzhou University, Lanzhou 730000, China
    Department of Geography, Western Michigan University, Kalamazoo, MI 49008, USA)

  • Chen Zhao

    (Department of Geography, Ohio State University, Columbus, OH 43220, USA)

Abstract

Accurate mapping the spatial distribution of different soil textures is important for eco-hydrological studies and water resource management. However, it is quite a challenge to map the soil texture in data scarce, hard to access mountainous watersheds. This paper compares a nonlinear method, the Markov chain random field (MCRF) with a classical linear method, ordinary kriging (OK) for calculating the soil texture at different search radiuses in the upstream region of the Heihe River Watershed. Results show that soil texture values that were calculated by the OK method tends to predict soil texture values within a certain range (sand (12.098~40.317), silt (47.847~71.231), and clay (12.781~19.420)) because of the smoothing effect, thus leading to greater accuracy in predicting the major soil texture type (silt loam). Nonetheless, the MCRF method considers the interclass relationships between sampling points, leading to greater accuracy in predicting minor types (loam and sandy loam). Meanwhile, the OK method performed best for all the types at the radius of 65 km influenced by the densities of all the sampling points, while the best performance of the MCRF method differs with radiuses as the largest densities varying for different soil types. For loam and sandy loam, the OK method ignored them, thus the MCRF method is more suitable in mountainous areas with high soil heterogeneity.

Suggested Citation

  • Jinlin Li & Lanhui Zhang & Chansheng He & Chen Zhao, 2018. "A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China," Sustainability, MDPI, vol. 10(8), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2819-:d:162769
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    References listed on IDEAS

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    1. Ling Lu & Chao Liu & Xin Li & Youhua Ran, 2017. "Mapping the Soil Texture in the Heihe River Basin Based on Fuzzy Logic and Data Fusion," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    2. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
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    Cited by:

    1. Huijuan Zhang & Wenkai Liu & Qiuxia Zhang & Xiaodong Huang, 2022. "Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    2. Jinlin Li & Lanhui Zhang, 2021. "Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area," Sustainability, MDPI, vol. 13(16), pages 1-18, August.

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