Estimation of Soil Organic Carbon Content at Various Moisture Levels Using Visible/Near-Infrared Spectroscopy

Document Type : Original Article

Authors

1 Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

2 Smart Agricultural Research Department, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran

3 Dep of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

4 Institute of Methodologies for Environmental Analysis, Italian National Research Council, Potenza, Italy

Abstract

Introduction: Determination of organic-carbon-content variation in the field is crucial due to the importance of soil organic carbon content, including its role in increasing soil resistance against wind and water erosion. This study examines the ability of reflectance visible-near infrared (Vis/NIR) spectrometry for measurement and prediction of soil organic carbon content and the effect of the type of spectral preprocessing on the accuracy of multivariable predictive models was studied.
Material and Methods: In this research, spectroscopy of soil samples was performed at 7 moisture levels in the interactance measurement mode in the 350-2500 nm spectral range using a contact probe. Spectrophotometry of 5 different sections of each soil sample was carried out and the data were processed and analyzed. Spectral data obtained from the spectrophotometer included unwanted information, background and noise in addition to the information of the samples. In order to arrive at accurate and reliable analytical models, pre-processing of the spectral data was required prior to regression model simulation. Multivariate calibration models of partial least squares (PLS) were developed based on the reference measurements and the information of the preprocessed spectra using a combination of different methods for assessment and prediction of soil organic carbon content. These included: smoothing (moving average (MA), and Savitzky-Golay (SG)); normalizing (multiplicative scatter correction (MSC), standard normal Variate (SNV)); as well as increasing the spectral resolution (first and second derivatives (D1, D2)).
Results and Discussion: Results showed that NIR spectroscopy is a suitable method for measurement of organic carbon content in soil samples. Prediction utilizing the data analyzed using the PLS model based on SG + MSC, produced the best detection results. Thus, SG+MSC preprocessing (Rc2 =0.81, RMSEC = 0.239, Rp2 = 0.79, RMSEP = 0.252) is suitable for predicting the amount of soil OC with high accuracy (SDR= 3.191). Results showed that reflectance rate diminishes with increasing moisture content reducing the ability of the PLS model to predict organic carbon content. This is true across all the preprocessing methods. In addition, the determined index values and validation criteria showed that prediction of organic carbon content with the PLS model using SG+D1+MSC, SG+MSC, SG+MSC, SG+D1+MSC, SG+SNV and SG+SNV combinations gives the best detection results for the following moisture levels, respectively: 6, 12, 18, 24, 30 and 36%.
Conclusion: Vis/NIR spectroscopy can be used as an alternative to conventional laboratory methods for soil organic-carbon-content determination. Results showed that the use of Vis/NIR spectroscopy for determination of soil organic carbon content can be considered in the site-specific management of fields, which can ultimately lead to saving inputs and reducing the pressure on the environment.
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Keywords

References

Aghaei Sadi, M., Minaei, S., Jamshidi, B. & Abdollahian Noghabi, M., 2018, Non-Destructive Evaluation of Sugar Content Using a Combination of Near-Infrared Spectroscopy (NIRS) and Chemometrics Methods, Iranian Journal of Biosystems Engineering, 49(1), PP, 9-18, DOI: 10.22059/ijbse.2017.208413.664785 (In Persian).
Aghaei Sadi, M., Minaei, S. & Jamshidi, B., 2021, Effect of SPECTRAL Pre-Processing mMethods on Developed Models for Nondestructiveprediction of Sugar Content in Sugar Beet and Design of a Detection System Based on Vis/NIR Spectroscopy, Journal of Researches in Mechanics of Agricultural Machinery, 10(1), PP. 69-81, https://jrmam.sku.ac.ir/ article-10015.html (In Persian).
Akbari, E., Mirzaei, S., Toomanian, A., Darvishi Boloorani, A. & Bahrami, H.A., 2022, Integrated Noise Reduction-Data Mining Method for Soil Organic Matter Prediction by VNIR Spectrometry, Journal of RS and GIS for Natural Resources, 13(3), PP. 1-21, https://doi.org/ 10.30495/girs.2022.1938557.1935.
Baumgardner, M.F., Silva, L.R.F., Biehl, L.L. & Stoner, E.R., 1986, Reflectance Properties of Soils, Advances in Agronomy, 38(C), PP. 1-44, https://doi.org/10.1016/S0065-2113 (08)60672-0.
Ben-Dor, E., Inbar, Y. & Chen, Y., 1997, The Reflectance Spectra of Organic Matter in the Visible Near-Infrared and Short Wave Infrared Region (400–2500 nm) during a Controlled Decomposition Process, Remote Sensing of Environment, 61(1), PP. 1-15, https://doi.org/10.1016/ S0034-4257(96)00120-4.
Bouma, J., 1997, Soil Environmental Quality: A European Perspective, Journal of Environmental Quality, 26(1), PP. 26-31, https://doi.org/10.2134/jeq1997.00472425002600010005x.
De Santana, F.B., de Giuseppe, L.O., de Souza, A.M. & Poppi, R.J., 2019, Removing the Moisture Effect in Soil Organic Matter Determination Using NIR Spectroscopy and PLSR with External Parameter Orthogonalization, Microchemical Journal, 145, PP. 1094-1101, https://doi.org/10. 1016/j.microc.2018.12.027.
Genot, V., Colinet, G., Bock, L., Vanvyve, D., Reusen, Y. & Dardenne, P., 2011, Near Infrared Reflectance Spectroscopy for Estimating Soil Characteristics Valuable in the Diagnosis of Soil Fertility, Journal of Near Infrared Spectroscopy, 19(2), PP. 117-138, https://doi.org/10.1255/jnirs.923.
Hadjimitsis, D., 2013, Remote Sensing of Environment: Integrated Approaches, BoD–Books on Demand.
Heise, H.M. & Winzen, R., 2001, Chemometrics in Near‐Infrared Spectroscopy, Near‐Infrared Spectroscopy: Principles, Instru-ments, Applications, Wiley, 125-162, https:// doi.org/10.1002/9783527612666.ch07.
Jamshidi, B., Minaei, S., Mohajerani, E. & Ghassemian, H., 2014, Prediction of Soluble Solids in Oranges Using Visible/Near-Infrared Spectroscopy: Effect of Peel, International Journal of Food Properties, 17(7), PP. 1460-1468, https:// doi.org/10.1080/10942912.2012.717332.
Janik, L.J., Forrester, S.T. & Rawson, A., 2009, The Prediction of Soil Chemical and Physical Properties from Mid-Infrared Spectroscopy and Combined Partial Least-Squares Regression and Neural Networks (PLS-NN) Analysis, Chemometrics and Intelligent Laboratory Systems, 97(2), PP. 179-188, https:// doi.org/10.1016/j.chemolab.2009.04.005.
Kodaira, M. & Shibusawa, S., 2013, Using a Mobile Real-Time Soil Visible-Near Infrared Sensor for High Resolution Soil Property Mapping, Geoderma, 199, PP. 64-79, https://doi.org/10.1016/j.geoderma. 2012.09.007.
Liu, Y., Sun, X., Zhou, J., Zhang, H. & Yang, C., 2010, Linear and Nonlinear Multivariate Regressions for Determination Sugar Content of Intact Gannan Navel Orange by Vis- NIR Diffuse Reflectance Spectroscopy, Math. Compute. Model., 51(11-12), PP. 1438-1443, https://doi.org/ 10.1016/j.mcm.2009.10.003.
 Minasny, B., McBratney, A.B., Bellon-Maurel, V., Roger, J.M., Gobrecht, A., Ferrand, L. & Joalland, S., 2011, Removing the Effect of Soil Moisture from NIR Diffuse Reflectance Spectra for the Prediction of Soil Organic Carbon, Geoderma, 167, PP. 118-120, https://doi.org/10.1016/j.geoderma. 2011.09.008.
Mirzaei, S., Hossein, A.B., Alavipanah, S.K. & Mousivand, A., 2020, Improving the Clay, Silt and Sand of Soil Prediction by Removing the Influence of Moisture on Reflectance Using EPO, Journal of Geospatial Information Technology, 8(2), PP. 59-74, http://dx.doi.org/10.29252/jgit. 8.2.59.
Mirzaei, S., Darvishi Boloorani, A., Bahrami, H.A., Alavipanah, S.K., Mousivand, A.J. & Mouazen, A.M., 2022, Minimising the Effect of Moisture on Soil Property Prediction Accuracy Using External Parameter Orthogonalization, Soil and Tillage Research, 215l, P. 105225, https://doi.org/10.1016/j.still.2021.105225.
Mirzaei, S., Casa, R., Guarini, R., Laneve, G., Marrone, L., Misbah, K., Pascucci, S., Pignatti, S., Rossi, F., Tricomi, A., 2024, Reduction of the Vegetation and Soil Moisture Effects to Improve Topsoil Properties Retrieval Accuracy from PRISMA Images, IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, 07-12 July, Athens, Greece (N. 5068), https://doi.org/10.1109/ IGARSS53475.2024.10642310.
Morellos, A., Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R., Tziotzios, G., Wiebensohn, J., Bill, R. & Mouazen, A.M., 2016, Machine learning Based Prediction of Soil Total Nitrogen, Organic Carbon and Moisture Content by Using VIS-NIR Spectroscopy, Biosystems Engineering, 152, PP. 104-116, https://doi.org/10.1016/ j.biosystemseng.2016.04.018.
Munnaf, M.A., Guerrero, A., Nawar, S., Haesaert, G., Van Meirvenne, M. & Mouazen, A.M., 2021, A Combined Data Mining Approach for On-Line Prediction of Key Soil Quality Indicators by Vis-NIR Spectroscopy, Soil and Tillage Research, 205(June 2020), P. 104808, https://doi.org/10.1016/j.still.2020.104808.
Nanni, M.R. & Demattê, J.A.M., 2006, Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis, Soil Science Society of America Journal, 70(2), PP. 393-407, https://doi.org/10.2136/sssaj2003.0285.
Nawar, S. & Mouazen, A.M., 2018, Optimal Sample Selection for Measurement of Soil Organic Carbon Using On-Line Vis-NIR Spectroscopy, Computers and Electronics in Agriculture, 151(May), PP. 469-477, https://doi.org/10.1016/j.compag.2018.06.042.
Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I. & Lammertyn, J., 2007, Nondestructive Measurement of Fruit and Vegetable Quality by Means of NIR Spectroscopy: A Review, Postharvest Biol. Technol., 46, PP. 99-118, https:// doi.org/10.1016/j.postharvbio.2007.06.024.
Pudełko, A. & Chodak, M., 2020, Estimation of Total Nitrogen and Organic Carbon Contents in Mine Soils with NIR Reflectance Spectroscopy and Various Chemometric Methods, Geoderma, 368(February), https://doi.org/10.1016/ j.geoderma.2020.114306.
Rasooli, N., Farpoor, M.H., Mahmoodabadi, M. & Esfandiarpour-Boroujeni, I., 2023, Vis-NIR Spectroscopy as an Eco-Friendly Method for Monitoring Pedoenvironmental Variations and Pedological Assessments in Lut Watershed, Central Iran, Soil and Tillage Research, 233, P. 105808, https://doi.org/10.1016/j.still.2023.105808.
Sharififar, A., Singh, K., Jones, E., Ginting, F.I. & Minasny, B., 2019, Evaluating a Low‐Cost Portable NIR Spectrometer for the Prediction of Soil Organic and Total Carbon Using Different Calibration Models, Soil Use and Management, 35(4), PP. 607-616,
       https://doi.org/10.1111/sum.12537.
Shepherd, K.D. & Walsh, M.G., 2002, Development of Reflectance Spectral Libraries for Characterization of Soil Properties, Soil Science Society of America Journal, 66(3), PP. 988-998, https://doi.org/10.2136/sssaj2002.9880.
Stenberg, B., 2010, Effects of Soil Sample Pretreatments and Standardised Rewetting as Interacted with Sand Classes on Vis-NIR Predictions of Clay and Soil Organic Carbon, Geoderma, 158(1-2), PP. 15-22, https://doi.org/10.1016/ j.geoderma.2010.04.008.
Viscarra Rossel, R.A., McGlynn, R.N., McBratney, A.B., Rossel, R.A.V., McGlynn, R.N. & McBratney, A.B., 2006, Determining the Composition of Mineral-Organic Mixes Using UV–vis–NIR Diffuse Reflectance Spectroscopy, Geoderma, 137(1-2), PP. 70-82, https://doi.org/10.1016/ j.geoderma.2006.07.004.

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