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Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As a typical region afflicted by soil salinization, the soda saline–alkali soils in the Songnen Plain of China demonstrate a clear cracking phenomena. Nevertheless, the overall spectral response to the cracked soil surface has scarcely been studied. This study intends to study the impact of salt parameters on the soil cracking process and enhance the spectral measurement method used for cracked salt-affected soil. To accomplish this goal, a controlled desiccation cracking experiment was carried out on saline soil samples. A gray-level co-occurrence matrix (GLCM) was calculated for the contrast (CON) texture feature to measure the extent of cracking in the dried soil samples. Additionally, spectroscopy measurements were conducted under different surface conditions. Principal component analysis (PCA) was subsequently performed to downscale the spectral data for band integration. Subsequently, the prediction accuracy of back-propagation artificial neural network (BP-ANN) models developed from the principal components of spectral reflectance was compared for different salt parameters. The results reveal that salt content is the dominant factor determining the cracking process in salt-affected soils, and that cracked soil samples had the highest model prediction accuracy for different salt parameters rather than uncracked blocks and 2 mm comparison soil samples. Furthermore, BP-ANN prediction models combining spectral response and CON were further developed, which can significantly enhance the prediction accuracy of different salt parameters with R2 values of 0.93, 0.91, and 0.74 and a ratio of prediction deviation (RPD) of 3.68, 3.26, and 1.72 for soil salinity, electrical conductivity (EC), and pH, respectively. These findings provide valuable insights into the cracking mechanism in salt-affected soils, thereby advancing the field of hyperspectral remote sensing for monitoring soil salinization. Furthermore, this study also aids in enhancing the design of spectral measurements for saline–alkali soils and is also helpful for local soil remediation with supporting data.

The precise quantification of soil salinity and the spatial distribution are paramount for proficiently managing and remediating salinized soils. This study aims to explore a pioneering methodology for forecasting soil salinity by combining the spectroscopy of soda saline–alkali soil with crack characteristics, thereby facilitating the ground-based remote-sensing inversion of soil salinity. To attain this objective, a surface cracking experiment was meticulously conducted under controlled indoor conditions for 57 soda saline–alkali soil samples from the Songnen Plain of China. The quantitative parameters for crack characterization, encompassing the length and area of desiccation cracks, together with the contrast texture feature were methodically derived. Furthermore, spectral reflectance of the cracked soil surface was measured. A structural equation model (SEM) was then employed for the estimation of soil salt parameters, including electrical conductivity (EC1:5), Na+, pH, HCO3−, CO32−, and the total salinity. The investigation unveiled notable associations between different salt parameters and crack attributes, alongside spectral reflectance measurements (r = 0.52–0.95), yet both clay content and mineralogy had little effect on the cracking process due to its low activity index. In addition, the presence of desiccation cracks accentuated the overall spectral contrast of salt-affected soil samples. The application of SEMs facilitated the concurrent prediction of multiple soil salt parameters alongside the regression analysis for individual salt parameters. Nonetheless, this study confers the advantage of the swift synchronous observation of multiple salt parameters whilst furnishing lucid interpretation and pragmatic utility. This study helps us to explore the mechanism of soil salinity on the surface cracking of soda saline–alkali soil in the Songnen Plain of China, and it also provides an effective solution for quickly and accurately predicting soil salt content using crack characteristics, which also provides a new perspective for the hyperspectral measurement of saline–alkali soils.

Hyperspectral technology is widely recognized as an effective method for monitoring soil salinity. However, the traditional sieved samples often cannot reflect the true condition of the soil surface. In particular, there is a lack of research on the spectral response of cracked salt-affected soils despite the common occurrence of cohesive saline soil shrinkage and cracking during water evaporation. To address this research, a laboratory was designed to simulate the desiccation cracking progress of 57 soda saline–alkali soil samples with different salinity levels in the Songnen Plain of China. After completion of the drying process, spectroscopic analysis was conducted on the surface of all the cracked soil samples. Moreover, this study aimed to evaluate the predictive ability of multiple linear regression models (MLR) for four main salt parameters. The hyperspectral reflectance data was analyzed using three different band screening methods, namely random forest (RF), principal component analysis (PCA), and Pearson correlation analysis (R). The findings revealed a significant correlation between desiccation cracking and soil salinity, suggesting that salinity is the primary factor influencing surface cracking of saline–alkali soil in the Songnen Plain. The results of the modeling analysis also indicated that, regardless of the spectral dimensionality reduction method employed, salinity exhibited the highest prediction accuracy for soil salinity, followed by electrical conductivity (EC) and sodium (Na+), while the pH model exhibited the weakest predictive performance. In addition, the usage of RF for band selection has the best effect compared with PCA and Pearson methods, which allows salt information of soda saline–alkali soils in Songnen Plain to be predicted precisely.

Soil salinity is one of the parameters used for determining the extent of soil salinization. During water evaporation, the surface of salt-affected soils in the Songnen Plain, China, exhibits obvious shrinkage and cracking phenomena due to the high salt content. The aim of this current study is to quantify the influence of the salt content on the surface shrinkage–cracking process and to achieve quantitative extraction of soil salinity parameters based on different crack parameter types. In order to achieve the above objectives, a controlled shrinkage–cracking experiment was conducted. Subsequently, three kinds of crack characteristics such as crack length, box-counting dimension, and 12 gray-level co-occurrence matrix (GLCM) texture features were quantitatively extracted from the standard binary crack patterns. In order to predict the soil physical–chemical properties of salt-affected soils in the Songnen Plain, three models such as multiple linear regression (MLR), multiple stepwise regression (MSR), and artificial neural network (ANN) were developed and compared based on the crack length, box-counting dimension, and the first two principal components of GLCM texture features. The results show that the extent of desiccation cracks was determined by soil salinity since the water film caused by exchangeable cations and the thickness of DDL determined by soil salinity can promote desiccation cracking. Although the three methods have high prediction accuracy for Na+, electrical conductivity (EC), and total soil salinity, the ANN-based method showed the best prediction with R2 values for Na+, EC, and soil salinity as high as 0.91, 0.91, and 0.89, and ratio of performance to deviation (RPD) values for Na+, EC, and soil salinity corresponding to 2.96, 3.47, and 2.95.

The surface of cohesive soda saline-alkali soil in the Songnen Plain of China exhibits obvious desiccation cracking phenomenon during water evaporation. Quantitative research on the process and influencing factors of desiccation cracking are significant for determining the mechanical properties of salt-affected soil, improving the salinization status and the local ecological environment. This study aims to conduct laboratory-controlled cracking experiments on soda saline-alkali soils, and to regularly measure the soil moisture and the cracking status of 40 soil samples. After processing the crack images uniformly, crack characteristics including crack length, crack area, contrast texture feature, and box-counting fractal dimension are extracted. The results indicate that the decrease in soil moisture can be divided into three stages such as fast evaporation stage, slow evaporation stage and stable evaporation stage, and that the evaporation rate shows a linear negative correlation with soil salinity. The measurements also indicate that crack length rapidly increases and reaches stability with a decrease in water content compared with the crack area. The high correlation between different crack characteristics and the soil total salinity (correlation coefficient from 0.44 to 0.95) shows that salt content is the dominant factor affecting the desiccation cracking of soda saline-alkali soil. A multiple linear regression prediction model was finally established based on crack characteristics for different salinity parameters with high prediction accuracy for Na+, EC and total salinity (R2 > 0.91 and RPD > 2.5) and also certain prediction accuracy for Cl− and CO32− (R2 > 0.66 and RPD > 1.5).