IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i8p2819-d162769.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/8/2819/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/8/2819/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    2. Juan Antonio Villarreal Sanchez & Lourdes Diaz Jimenez & Jose Concepcion Escobedo Bocardo & Jose Omar Cardenas Palomo & Nereida Elizabeth Guerra Escamilla & Jesus Salvador Luna Alvarez, 2018. "Effect of Marine Microorganisms on Limestone as an Approach for Calcareous Soil," Sustainability, MDPI, vol. 10(6), pages 1-11, June.
    3. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
    4. Zhang, Jin-hua & Liu, Yong-qian & Tian, De & Yan, Jie, 2015. "Optimal power dispatch in wind farm based on reduced blade damage and generator losses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 64-77.
    5. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.
    6. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    7. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2013. "First and second order semi-Markov chains for wind speed modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1194-1201.
    8. Khasanzoda, Nasrullo & Zicmane, Inga & Beryozkina, Svetlana & Safaraliev, Murodbek & Sultonov, Sherkhon & Kirgizov, Alifbek, 2022. "Regression model for predicting the speed of wind flows for energy needs based on fuzzy logic," Renewable Energy, Elsevier, vol. 191(C), pages 723-731.
    9. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    10. Yousif Alyousifi & Kamarulzaman Ibrahim & Mahmod Othamn & Wan Zawiah Wan Zin & Nicolas Vergne & Abdullah Al-Yaari, 2022. "Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
    11. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    12. Coville, Aidan & Siddiqui, Afzal & Vogstad, Klaus-Ole, 2011. "The effect of missing data on wind resource estimation," Energy, Elsevier, vol. 36(7), pages 4505-4517.
    13. Carapellucci, Roberto & Giordano, Lorena, 2013. "The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants," Applied Energy, Elsevier, vol. 107(C), pages 364-376.
    14. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    15. Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    16. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
    17. Scholz, Teresa & Lopes, Vitor V. & Estanqueiro, Ana, 2014. "A cyclic time-dependent Markov process to model daily patterns in wind turbine power production," Energy, Elsevier, vol. 67(C), pages 557-568.
    18. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    19. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
    20. Jujie Wang & Yanfeng Wang & Yaning Li, 2018. "A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction," Energies, MDPI, vol. 11(2), pages 1-33, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2819-:d:162769. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.