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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

by Jinlin Li 1,2, Lanhui Zhang 1,2,*, Chansheng He 1,2,3,* and Chen Zhao 4
1
Key Laboratory of West China’s Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2
Center for Dryland Water Resources Research and Watershed Science, Lanzhou University, Lanzhou 730000, China
3
Department of Geography, Western Michigan University, Kalamazoo, MI 49008, USA
4
Department of Geography, Ohio State University, Columbus, OH 43220, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2018, 10(8), 2819; https://doi.org/10.3390/su10082819
Received: 7 July 2018 / Revised: 2 August 2018 / Accepted: 5 August 2018 / Published: 9 August 2018
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. View Full-Text
Keywords: Markov chain random field; ordinary kriging; soil texture interpolation; The Heihe River Watershed Markov chain random field; ordinary kriging; soil texture interpolation; The Heihe River Watershed
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Li, J.; Zhang, L.; He, C.; Zhao, C. A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China. Sustainability 2018, 10, 2819.

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