University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 52 4 2021 06 22 Assessing the Visible–Near-Infrared Spectroscopy Method and PLSR and SVMR Methods in Modeling Organic Carbon and Total Neutralizing Value of Soil Assessing the Visible–Near-Infrared Spectroscopy Method and PLSR and SVMR Methods in Modeling Organic Carbon and Total Neutralizing Value of Soil 1011 1023 81775 10.22059/ijswr.2021.311456.668759 FA Rokhsar Akbarifazli Department of Soil Science. Khoozestan Science and Research Branch, Islamic Azad University, Ahvaz, Iran Teimour Babaeinejad Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran Navid Ghanavati Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran Akbar Hasani Department of soil science, faculty of Agriculture, university of Zanjan, Zanjan, Iran Mohammad Sadegh Askari Department of soil science, faculty of Agriculture, university of Zanjan, Zanjan, Iran Journal Article 2020 10 07 For sustainable land management, it is necessary to understand the functions and characteristics of soils, and their spatial and temporal changes. Visible and near-infrared spectroscopy has a specific capability to identify and determine soil properties due to high accuracy and high-performance speed. The purpose of this study is to evaluate the accuracy of visible and near-infrared spectroscopy method in estimating soil organic matter and total neutralizing value. Therefore, 110 soil samples were collected from Khuzestan, Yazd and Tehran provinces, and spectral reflectance was performed using ASD FieldSpec3. The spectra obtained from the spectrometer were pre-processed using five methods including Savitzky-Golay filter (SG), the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variate (SNV), and Multiplicative scatter correction (MSC). Also, the performance of PLSR and SVMR methods was compared in terms of soil organic carbon and total neutralizing value estimation. The results indicated that the PLSR model in estimating both organic carbon properties and total neutralizing value had higher accuracy compared to the SVR model. In estimation of soil organic carbon, PLSR method and MSC preprocessing method had the best performance (R<sup>2</sup><sub>VAL</sub>=0.59, RMSE<sub>VAL</sub>=0.19 and PRD<sub>VAL</sub>=1.47) and the second derivative method had the least performance (R<sup>2</sup><sub>VAL</sub>=0.15, RMSE<sub>VAL</sub>=0.27 and PRD<sub>VAL</sub>=0.52). Also for estimation of total neutralizing value, the first derivative preprocessing method had the best performance (R<sup>2</sup><sub>VAL</sub>=0.78, RMSE<sub>VAL</sub>=5.70 and PRD<sub>VAL</sub>=2.01) and the second derivative method had the least performance (R<sup>2</sup><sub>VAL</sub>=0.1, RMSE<sub>VAL</sub>=11.13, and PRD<sub>VAL</sub>=0.31). The key wavelengths were observed for soil organic matter in the range of 421- 612 nm and for total neutralizing value in the range of 2315- 2151 nm. This study showed that the Vis-NIR spectroscopy method, due to its physical basis and considering the influencing factors, as a large-scale model, makes it possible to evaluate and predict soil OC and TNV. For sustainable land management, it is necessary to understand the functions and characteristics of soils, and their spatial and temporal changes. Visible and near-infrared spectroscopy has a specific capability to identify and determine soil properties due to high accuracy and high-performance speed. The purpose of this study is to evaluate the accuracy of visible and near-infrared spectroscopy method in estimating soil organic matter and total neutralizing value. Therefore, 110 soil samples were collected from Khuzestan, Yazd and Tehran provinces, and spectral reflectance was performed using ASD FieldSpec3. The spectra obtained from the spectrometer were pre-processed using five methods including Savitzky-Golay filter (SG), the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variate (SNV), and Multiplicative scatter correction (MSC). Also, the performance of PLSR and SVMR methods was compared in terms of soil organic carbon and total neutralizing value estimation. The results indicated that the PLSR model in estimating both organic carbon properties and total neutralizing value had higher accuracy compared to the SVR model. In estimation of soil organic carbon, PLSR method and MSC preprocessing method had the best performance (R<sup>2</sup><sub>VAL</sub>=0.59, RMSE<sub>VAL</sub>=0.19 and PRD<sub>VAL</sub>=1.47) and the second derivative method had the least performance (R<sup>2</sup><sub>VAL</sub>=0.15, RMSE<sub>VAL</sub>=0.27 and PRD<sub>VAL</sub>=0.52). Also for estimation of total neutralizing value, the first derivative preprocessing method had the best performance (R<sup>2</sup><sub>VAL</sub>=0.78, RMSE<sub>VAL</sub>=5.70 and PRD<sub>VAL</sub>=2.01) and the second derivative method had the least performance (R<sup>2</sup><sub>VAL</sub>=0.1, RMSE<sub>VAL</sub>=11.13, and PRD<sub>VAL</sub>=0.31). The key wavelengths were observed for soil organic matter in the range of 421- 612 nm and for total neutralizing value in the range of 2315- 2151 nm. This study showed that the Vis-NIR spectroscopy method, due to its physical basis and considering the influencing factors, as a large-scale model, makes it possible to evaluate and predict soil OC and TNV. Organic carbon Preprocessing Regression Spectroscopy Total neutralizing value https://ijswr.ut.ac.ir/article_81775_0996acdd11da6c450be4be93baeea3e8.pdf