عنوان مقاله [English]
Introduction Soil salinity and alkalization are recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. To manage these soils a lot of data are needed and laboratory measurement is costly and time-consuming. Therefore, indirect methods that are cheap, fast and easy to access are one of the research priorities. One of these methods is visible near infrared diffuse reflectance spectroscopy. Visible and near infrared diffuse reflectance spectroscopy is a time and cost-effective approach that has been successfully used for characterizing soil properties.
Materials and Methods The study area is located in Kurdistan Province, about 20 km northeast of Ghorveh city, west of Iran, and covers 260 km2. Average annual precipitation and temperature are 369.8mm and 10.8 °C, respectively. Soil moisture and temperature regimes are Xeric and Mesic, respectively. In the study area, 100 soil samples were collected (0–30 cm depth). The main land use types consist of cropland and rangeland. The soil samples were air-dried at room temperature and then, passed through a 2mm sieve. EC, pH, SAR, OC, CaCO3 and ΔMWD were measured. Sodium Adsorption Ratio (SAR) was calculated using results from the saturated paste extracts of sodium, calcium, and magnesium. The stability aggregate was measured using the difference between distributions of particle size in dry and wet sieve methods. Spectral analysis of soil samples was done using a spectrophotometric instrument with a wavelength of 350 to 2500 nm and recorded using RS3 software. After recording the spectra, different preprocessing methods were evaluated. Two models of multiple linear regression and artificial neural network were used to predict soil properties using spectral data.
Results and Discussion The soil salinity of the study area ranged between low and high. The highest amount of salinity was observed in the center, south and southwest of the study area and the least amount of salinity was observed in northwest, southeast, northeast and north. The maximum amounts of acidity and sodium adsorption ratio showed that the central part of the study area has saline and sodium soils. The results showed that the best method for preprocessing of spectral data is the 1st Derivative + Savitzky-Golay filter + Mean center + SNV. The Pearson correlation coefficient between the soil properties and the spectral reflection values for each wavelength in the range of 2450-400 nm showed that there is a relatively high correlation between the measured characteristics and the spectral values of the soil. The results showed that the correlation coefficient can be positive or negative. The maximum positive correlation coefficients for electrical conductivity, soil acidity, sodium adsorption, organic carbon, calcium carbonate and aggregate stability at the wavelengths 1229, 2397, 2399, 1298, 2090, 2014, and two spectra 2257 and 660 were 0.45**, 0.43**, 0.46**, 0.61**, 0.53** and 0.40**, respectively. The maximum negative correlation coefficients for electrical conductivity, soil acidity, sodium adsorption ratio, organic carbon, calcium carbonate and aggregate stability at the wavelengths 630, 2289, 630, 1904, 1379 and 2107 were -0.47**, -0.42**, -0.44**, -0.46**, -0.55** and -0.44**, respectively. Based on the determination coefficient statistic, artificial neural network model (0.88, 0.25, 0.59, 0.68, 0.52 and 0.48 to electrical conductivity, PH, SAR, calcium carbonate and aggregate stability, respectively) had better results compared to the multiple linear regression model (0.45, 0.13, 0.23, 0.66, 0.48 and 0.28 to electrical conductivity, PH, SAR, calcium carbonate and aggregate stability, respectively).
Conclusion In this study, visible near infrared diffuse reflectance spectroscopy was evaluated to estimate some properties of salt-affected soils. After recording the spectral data, the continuity curve and pre-processing of spectral data were performed. The results showed that the best method for pre-processing of spectral data is the first derivative + Savitzky filter and Glair + Mid filter + Normal standard variable. Multiple linear regression and artificial neural network models were used to estimate some properties of salt-affected soils (EC, pH, SAR, OC, CaCO3 and ΔMWD) using spectral data. Based on the statistics of mean error, root mean squared error, and correlation coefficient, the artificial neural network model had better results in estimateing the properties of salt-affected soils compared to the multiple linear regression model. Therefore, based on these findings it is suggested that soil spectral data be used as an indirect method to the estimate soil properties.