Abstract
To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.
Highlights
Soil is a valuable resource on which humans depend for survival
Lihua Xu et al employed laboratory reflectance spectra of 42 soil samples to compare four pre-treatment methods and found that first-order derivative (FD) combined with the principal component (PC)-transformed multiple linear regression (MLR) method resulted in a better total potassium (TK) prediction model [34]
Rossel R et al compared multiple linear regression (MLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), support vector machines (SVMs), random forests (RFs), boosted trees (BTs), and artificial neural networks (ANNs) to estimate soil organic carbon (SOC), clay content (CC), and pH measured in water [39]
Summary
Soil is a valuable resource on which humans depend for survival. As the area of arable land continues to decrease with increasing social development and the pressure on areas due to increases in food cultivation, saline land has become an effective arable land back-up resource. Zipeng Zhang et al compared nine pre-processing methods based on 233 soil samples retrieved from Xinjiang, Northwest China, including SG smoothing, discrete wavelet transform (DWT), first-order derivative (FD), second-order derivative (SD), MSC, standard normal variate and detrending (SNV-DT), and continuous peak removal (CR). Lihua Xu et al employed laboratory reflectance spectra of 42 soil samples to compare four pre-treatment methods and found that FD combined with the principal component (PC)-transformed multiple linear regression (MLR) method resulted in a better total potassium (TK) prediction model [34]. Rossel R et al compared multiple linear regression (MLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), support vector machines (SVMs), random forests (RFs), boosted trees (BTs), and artificial neural networks (ANNs) to estimate soil organic carbon (SOC), clay content (CC), and pH measured in water (pH) [39]. Final inversion of the salt content of saline soils was achieved based on PLSR, the radial basis function (RBF) neural network algorithm, and the RF algorithm
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