IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i11p6556-d825936.html
   My bibliography  Save this article

Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil

Author

Listed:
  • Yue Zhao

    (Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China)

  • Zhuopeng Zhang

    (Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China)

  • Honglei Zhu

    (College of Life Science, Henan Normal University, Xinxiang 453007, China)

  • Jianhua Ren

    (Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China)

Abstract

Desiccation cracking during water evaporation is a common phenomenon in soda saline–alkali soils and is mainly determined by soil salinity. Therefore, quantitative measurement of the surface cracking status of soda saline–alkali soils is highly significant in different applications. Texture features can help to determine the mechanical properties of soda saline–alkali soils, thus improving the understanding of the mechanism of desiccation cracking in saline–alkali soils. This study aims to provide a new standard describing the surface cracking conditions of soda saline–alkali soil on the basis of gray-level co-occurrence matrix (GLCM) texture analysis and to quantitatively study the responses of GLCM texture features to soil salinity. To achieve this, images of 200 field soil samples with different surface cracks were processed and calculated for GLCMs under different parameters, including directions, gray levels, and step sizes. Subsequently, correlation analysis was then conducted between texture features and electrical conductivity (EC) values. The results indicated that direction had little effect on the GLCM texture features, and that four selected texture features, contrast (CON), angular second moment (ASM), entropy (ENT), and homogeneity (HOM), were the most correlated with EC under a gray level of 2 and step size of 1 pixel. The results also showed that logarithmic models can be used to accurately describe the relationships between EC values and GLCM texture features of soda saline–alkali soils in the Songnen Plain of China, with calibration R 2 ranging from 0.88 to 0.92, and RMSE from 2.12 × 10 −4 to 9.68 × 10 −3 , respectively. This study can therefore enhance the understanding of desiccation cracking of salt-affected soil to a certain extent and can also help to improve the detection accuracy of soil salinity.

Suggested Citation

  • Yue Zhao & Zhuopeng Zhang & Honglei Zhu & Jianhua Ren, 2022. "Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6556-:d:825936
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/11/6556/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/11/6556/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jianhua Ren & Kai Zhao & Xiangwen Wu & Xingming Zheng & Xiaojie Li, 2018. "Comparative Analysis of the Spectral Response to Soil Salinity of Saline-Sodic Soils under Different Surface Conditions," IJERPH, MDPI, vol. 15(12), pages 1-13, December.
    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. Elio Romano & Massimo Brambilla & Carlo Bisaglia & Alberto Assirelli, 2023. "Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms," Agriculture, MDPI, vol. 13(6), pages 1-13, May.
    2. Sheshu Zhang & Jun Zhao & Jianxia Yang & Jinfeng Xie & Ziyun Sun, 2024. "Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China," Land, MDPI, vol. 13(6), pages 1-21, June.

    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. Kai Li & Haoyun Zhou & Jianhua Ren & Xiaozhen Liu & Zhuopeng Zhang, 2024. "A Comparative Study of Different Dimensionality Reduction Algorithms for Hyperspectral Prediction of Salt Information in Saline–Alkali Soils of Songnen Plain, China," Agriculture, MDPI, vol. 14(7), pages 1-20, July.

    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:jijerp:v:19:y:2022:i:11:p:6556-:d:825936. 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.