## 4 Conclusions  
This study aimed to investigate the regression model of soil property prediction by using a DRS preprocessing method after collecting soil samples from saltwater paddy fields in Korea. A total of 120 soil samples from saltwater paddy fields were collected for property analysis, spectral data were collected for the dry and wet soils, and PLSR analysis was then performed by applying SG smoothing and SNV preprocessing methods.  
For the soil property prediction models of the wet soil collected from the fields that underwent SG smoothing and SNV preprocessing, both resulted in a poor model. However, when SG smoothing was applied to the dry clay, pH, EC, and TN, they all met the criteria for a fair model in the VIS spectral region, while clay met the criteria for a fair model in the NIR spectral region. In addition, when SNV preprocessing was applied to the dry clay, clay met the criteria for a good model in the VIS spectral region, while pH, EC, and TN resulted in a fair model. For the B-matrix wavelengths of dry clay, only the pH and TOC properties with SNVs showed an increasing trend in the 1350-1450 nm region, while the other soil property values decreased. In addition, all the soil properties that underwent SG smoothing and SNV preprocessing showed similar wavelength trends in the 1800-1950 nm region.  
The results of the analysis of dry and wet soils showed that the regression model for predicting soil properties that underwent SNV preprocessing was statistically better than the regression model that underwent SG smoothing, and that the SNV method was more favorable in PLSR analysis with a single round of preprocessing. Collecting data under various soil conditions and researching more preprocessing techniques are expected to improve the accuracy of the soil property predicting models. Additionally, compared to traditional soil analysis methods, this approach is considered more efficient in terms of time, labor, and cost, and it is expected to enhance the reliability and safety of real-time soil property measurements in the field. However, the research on utilizing DRS for soil property based on each preprocessing is not enough in South Korea. Further research is needed on the performance of various preprocessing, such as SNV, SG, and others, with additional data.  
Conceptualization, Y.C. and J.S.; methodology, W.-J.C.; software, M.Y.; validation, J.S., D.-C.K. and Y.C.; formal analysis, Y.C. and M.Y.; data curation, D.-C.K. and W.-J.C.; writing--original draft preparation, J.S. and Y.C.; writing--review and editing, Y.C. and M.Y.; project administration, Y.C. and D.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Rural Development Administration of Korea (RS-2022-RD010389) and the National Institute of Agriculture, Forestry, and Food Technology Planning and Evaluation (RS-2023-00236201). This research was supported by \"Research Base Construction Fund Support Program\" funded by Jeonbuk National University in 2024.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.