## 3 Results and Discussion
### Comparison of Regression Models Predicting Soil Properties Using Preprocessing  
The analysis of dry and wet soils showed that the regression model of soil property predictions with the SNV was statistically better than the regression model with SG smoothing, and the SNV was more favorable for PLSR analysis with a single round of preprocessing (Tables 2 and 3). The PSLR analysis of the model of dry soil with SG smoothing showed that pH and clay met the evaluation criteria of a fair model with RPD values of 1.43-1.87 and 1.67-1.90 in the VIS and NIR spectral regions, respectively, while EC and TN showed a fair model with RPD values of 1.60 and 1.43 in the VIS spectral region, respectively. The regression model of dry soil showed a minimum of 7% and a maximum of 72% increase in the RPD values, which were comparable to those of the wet soil. The PSLR analysis of the model of clay with SNVs resulted in an RPD of 1.72-2.21 for clay, which met the evaluation criteria of a good model in the VIS spectral region and a fair model in the NIR spectral region. The pH RPD of 1.40-1.96 met the criteria for a fair model in both the VIS and NIR spectral regions, and the TN RPD of 1.44 met the criteria for a fair model in the VIS spectral region, but all the other soil property regression models were poor. The regression model of dry soil showed a statistical increase in the RPD values from a minimum of about 5% to a maximum of about 86%, which were comparable to those of the wet soil. Overall, the PLSR analysis results of the wet soils showed low statistical values. The moisture content of soil is an important factor that has different effects on the spectra and reduces the prediction accuracy [35; 36; 37]. Therefore, the soil samples collected from the four fields had different soil moisture distributions, which may explain the lower PSLR analysis results for wet soils compared to those of the dry soils. When developing a regression model for predicting the properties of wet soils, the model with an appropriate number of factors showed increasing R2 and RPD values, which were comparable to the dry soil model with the same number of factors. However, for the model that underwent SG smoothing, the RPD values decreased in the NIR spectral region of TN; this trend is believed to be caused by the predictive value of the model being larger than the variability in the measured data. PLSR analysis generates a regression coefficient, the B-matrix coefficient (beta coefficient), which can be used to identify which wavelengths in the spectrum are most influential in the regression model. The B-matrix represents the strength of the latent variables that can directly or indirectly influence the dependent variable. Figure 4 shows the wavelengths of the B-matrix after applying SG smoothing and SNV preprocessing on clay soil. Overall, the B-matrix wavelengths of the dry clay showed similar trends in the bands with ranges of 1350-1450 nm and 1800-1950 nm, but only the pH and TOC properties with an SNV increased in the 1350-1450 nm region. In the 1800-1950 nm region, the wavelengths of all the soil properties with SG smoothing and SNV preprocessing showed similar trends, while the other soil properties decreased. Figure 5 shows dotted line plots for the top four property regression models with SG smoothing and SNV preprocessing of dry clay, with the properties of pH, EC, TN, and clay having the best statistical values in both the regression models. Compared to the regression model that underwent SG smoothing, the regression model with SNVs showed generally better results. For TN, there was no significant difference between the results of SG smoothing (RPD = 1.43, R\({}^{2}\) = 0.51) and SNV preprocessing (RPD = 1.44, R\({}^{2}\) = 0.55), but for clay, the model with the SNV (RPD = 2.21, R\({}^{2}\) = 0.80) had about a 16% higher RPD compared to that of the model that underwent SG smoothing (RPD = 1.90, R\({}^{2}\) = 0.72). Figure 4: PLS B-matrix wavelengths of dry soil using (**a**) SG smoothing and (**b**) SNV preprocessing.