Desiccation cracking is a very common surface soil phenomenon of saline-sodic land. The objective of this study was to investigate the effects of salt content on the spectral reflectance of soil with and without desiccation cracks. To achieve our objective, a cracking test was performed using 17 soil samples. Following the tests, crack parameters were extracted, and correlation analysis was then performed between crack parameters and four soil properties: Na⁺, salinity (total concentration of ions), pH, and electric conductivity (EC). In order to select the optimum spectral measurement method and develop prediction models, spectral response to different soil properties were compared between the cracked soil samples and the comparative soil samples composed of the 2 mm particle size fraction processed by traditional methods. The results indicate that soil salinity dominated cracking propagation with a positive correlation. Since area and volume scattering are closer to what occurs in the field, a greater spectral response to soil properties was found for cracked soil samples relative to the comparative soil samples in the near-infrared and shortwave-infrared regions. The R² of optimal linear prediction models based on the cracked soil samples were 0.74, 0.67, 0.58, and 0.67 for Na⁺, salinity, pH, and EC, respectively.

The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na⁺, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2–11% decrease in the parameters mean absolute error (MAE).