Comparative study on the abilities of different crack parameters to estimate the salinity of soda saline-alkali soil in Songnen Plain, China

• Published in: Catena (Giessen) 2022-06, Vol.213, p.106221, Article 106221
• Main Authors: Ren, Jianhua, Xie, Ruifeng, Zhu, Honglei, Zhao, Yue, Zhang, Zhuopeng
• Format: Article
• Language: English
• Subjects: Crack intensity factor; Fractal dimension; Soda saline-alkali soil; Songnen Plain; Texture feature
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2022.106221

[Display omitted] •Different types of crack parameters extracted from saline-alkali soils and compared.•Effect of equal partition on box-counting dimensions extracted from soil cracks.•Relationships of 4 GLCM texture features computed from binary cracked patterns.•Prediction models of EC values developed based on different crack parameters. Salinity is commonly considered to be the main factor determining the crack extent of clayey soda saline-alkali soil in the Songnen Plain in China. However, there are great variations among different parameters in quantifying the extent of desiccation cracks in salt-affected soil, leading to substantial uncertainty in their abilities to characterize soil salinity. This study aimed to analyze the relationships between different types of crack parameters and the salinity of soil samples to establish optimal prediction models for the electrical conductivity (EC) values. To achieve these objectives, 104 soil samples with different salinity levels were obtained and pre-processed based on a unified standard for binary crack patterns. Thereafter, three common types of crack parameters including the crack intensity factor (CIF); box-counting dimensions under equal-partitions of 2, 3, 5, and 7; and four gray-level co-occurrence matrix (GLCM) texture features were extracted for correlation coefficients with the EC values of the soil samples based on a series of regression analyses. The results indicate that although different types of crack parameters all have a clear correlation with EC, CIF shows a poor relationship with EC values compared to those of the fractal dimensions and the GLCM texture features. The CIF, box-counting fractal dimensions based on 2 equal-partition, and contrast (CON) texture feature were then selected as the most ideal indices to develop exponential regression models for EC estimation. In addition, fitting results from verification work showed that CON was the best predictor of EC (R2 of 0.85), with an accuracy much higher than box-counting dimension based on 2 equal-partition (R2 of 0.68) and CIF (R2 of only 0.56).