Estmiating of Soil Particles Percentage Using Visible-Near Infra-Red (NIR) spectrometry in Semirom area, Isfahan

Document Type : Research Paper

Authors

1 Ph.D. Student, Department of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran,.

2 Associate Professor, Department of Soil Science, College of Agriculture, Shahid Chamran University of Ahvaz

3 Professor, Department of Remote Sensing and GIS, Faculty of Earth Science, Shahid Chamran University of Ahvaz,

4 Professor, Department of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran,

Abstract

 
The present research performed to estimate soil texture using visible near-infrared spectrometry in Semirom, Isfahan. A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory using hydrometry method. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction and Standard Normal Variable were applied and performed on spectral data. The Partial Least Squares Regression, Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The best result was obtained for Silt estimation, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively. In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses.

Keywords

Main Subjects

Estmiating of Soil Particles Percentage Using Visible-Near Infra-Red (NIR) spectrometry in Semirom area, Isfahan

EXTENDED ABSTRACT

Introduction

Soil texture, describing the relative proportion of sand, silt and clay in the mineral phase of soils is a major determinant of its water storage capacity and permeability, aeration, bulk density, aggregate stability and carbon storage capacity. Soil texture measurement on large scales using experimental methods can be extremely time-consuming and expensive, especially when dealing with a high spatial sampling density. Soil Visible and Near-Infra Red (V-NIR) reflectance spectroscopy has proven to be a fast, cost-effective, nondestructive, environmental-friendly, repeatable, and reproducible analytical technique. V-NIR reflectance spectroscopy has been used more than 30 years to predict an extensive variety of soil properties like organic and inorganic carbon, nitrogen, organic carbon, moisture, texture and salinity. The objective of this study was to estimate soil texture using visible near-infrared and short-wave Infrared (SWIR) reflectance spectroscopy (350-2500 nm) in Semirom, Isfahan. In this study, the best predictions of all the soil particles percentage, model and pre-processing technique were also determined. The Partial Least Squares Regression (PLSR), Support Vector Machine Regression and Artificial Neural Network models were also compared to estimate soil texture.

 

Materials and Methods

A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First (FD) Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction (MSC) and Standard Normal Variable (SNV) were applied and were performed on spectral data. The Partial Least Squares Regression (PLSR), Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The selection of the best model was done according to the value of the Residual Prediction Deviation (RPD), the coefficient of determination (R2), and the Root MeanSquare Error (RMSE).

 

Results and Discussion

Coefficient of Variation (CV) values indicated that the variability of clay and silt were medium. However, the variability of Sand was high. The soil property of best Result was Silt, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively.

 

Conclusions

In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses. Further, spectroscopy could be considered as a simple, fast, and low-cost method in predicting soil properties.

 

 

References

Ahmadi, A.; Emami, M.; Daccache, A. & He, L. (2021). Soil Properties Prediction for Precision Agriculture Using Visible and Near-Infrared Spectroscopy: A Systematic Review and Meta-Analysis. Agronomy, 11)433:( 1-14.
Almeida, J., & Predictive, S. (2002). Non-linear modeling of complex data by artificial neural networks. Current Opinion in Biotechnology, 13: 72-6.
Bouyoucos, G. J. (1951). A recalibration of hydrometer method for making mechanical analysis of soil. Agronomy, 43: 434-438.
Camo, A. 1998. The Unscrambler User Manual. CAMO ASA Norway.
Chaternour, M., Landi, A., Farrokhian Firouzi, A., Noroozi, A.A., Bahrami, H.A. 2020.  Spectral behavior modeling of soil texture over dust center of Khuzestan Province using hyperspectral images and Random Forest (RF) model. Advanced applied Geology, 9 (4): 466-479.
Chin, W. W., Marcolin, B., & Newsted, P. (1996). A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and voice mail emotion/adoption study, In proceeding of the 17th international conference on information systems, 16-18 Dec. 1996, Cleveland, Ohio, 21-41.
Clark, R.N. 1999. Spectroscopy of rocks and minerals, principles of spectroscopy. John Wiley and Sons.
Curcio, D. G., Ciraolo, F. & Minacapilli, M. (2013). Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences, 19: 494-503.
Dotto, A. C., Dalmolin, R. S. D., Pedron, F. d. A., Caten, A. T., & Ruiz, L. F. C., (2014). Digital mapping of soil properties: particle size and soil organic matter by diffuse reflectance spectroscopy. Revista Brasileira de Ciência do Solo, 38: 6. 1663-1671.
Gras J.P., Barthès, B.G. Mahaut B. & Trupin. S. (2014). Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoil. Geoderma. 215: 126–134.
Islam, K., Singh, B., & McBratney, A. (2003). Simultaneous estimation of several soilproperties by ultraviolet, visible and near infrared reflectance spectroscopy. Australian Journal of Soil Research, 41: 1193–1202.
Jalalian, A. (1997). The studies of land resources and capability determination in Semirom area. The Ministry of Jahad Sazandegi, Isfahan Province. (in Persian)
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. 179-188.
Karayiannis N. B., & Venetsanopouios A. N. (1993). Artificial Neural Network: learning algorithms, performance evaluation, & application. Kluwer academic publisher. boston.
Kuśnierek, K. (2011). Pre-processing of soil visible and near infrared spectra taken in laboratory and field conditions to improve the within-field soil organic carbon multivariate calibration, The Second Global Workshop on Proximal Soil Sensing, Montreal, Canada, 100-103.
Lacerda, M. P. C., Demattê, J. A. M., Sato, M. V., Fongaro, C. T., Gallo, B. C., & Souza, A. B. (2016). Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification. Remote Sensing, 8: 701. 1-20.
Lazaar, A., Pradhan, B., Naiji, Z., Gourfi, A., El Hammouti, K., Andich, K., & Monir, A. (2021). The manifestation of VIS-NIRS spectroscopy data to predict and map soil texture in the Triffa plain (Morocco). Kuwait Journal of Science, 48: 1. 127-137.
Mehrabi Gohari1, E. Matinfar, H.R. Taghizadeh-Mehrjardi, R.A. & Jafari, A. 2022. Visible-Near Infrared (VIS-NIR) Spectrophotometry in Predicting Soil Particle Percentage Using Artificial Neural Network and Partial Least Squares Regression. Journal of Water and Soil, 34(3): 623-635.
Lanyon, L.E. & Heald W. R. (1982). Magnesium, calcium, strontium and barium. P. 247-260. In: A. L., Page et al. (ed.), Methods of Soil Analysis. Part2, Agron. Monogr. ASA and SSSA, Madison, Wisconsin.
Minasny, B., & McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32: 9. 1378-1388.
Mouazen, A. M., Kuang, B., D. E. Baerdemaeker & Ramon, H. (2010). Comparison between principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 158: 23-3.
Nelson, R.E. (1982). Carbonate and gypsum. In: A. L. Page et al. (eds.), Methods of Soil Analysis (2nd ed). Part 2, Agronomy Monogaraph. No: 9. ASA and SSSA, Madison, Wisconsin. 181-196.
Nawar, S., Buddenbaum, H., Hill, J., Kozak, J., & Mouazen, A. (2016). Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil and Tillage Research, 155: 510-522.
Richards, L.A. (1954). Diagnosis and Improvement of Saline-Alkali Soils.US Departent of Agriculture, Washington DC.
Rahmati, F., Hojati, S., Rangzan K., & Landi, A. (2022). Investigating the Efficiency of Visible-Near Infra-Red (NIR) Spectrometry to Estimate Selected Soil Properties in Semirom Area, Isfahan. Journal of Water and Soil, 36(2): 283-300. (In Persian)
Rasooli, N., Farpoor M., Khayamim F., and Ranjbar H. (2018). Prediction of selected soil properties using visible and near infrared spectroscopy in Bardsir area, Kerman Province. Iranian Journal of Soil Research, 32(2): 231-243. (in Persian)
Reeves, J., McCarty G. & Mimmo, T. (2002). The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental Pollution, 116: 277–284.
Rinnan, A., Van den Berg, F., & Engelsen, S. B. (2009). Review of the most common preprocessing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry, 28: 1201–1222.
Tsai, F. and W. Philpot. (1998). Derivative analysis of hyperspectral data. Remote Sensing Environment, 66: 41–51.
Sargent D.J. (2001). Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer, 91:1636-42.
Silva, E. B., Ten Caten, A., Dalmolin, R. S. D., Dotto, A. C., Silva, W. C., & Giasson, E. (2016). Estimating Soil Texture from a Limited Region of the Visible/Near-Infrared Spectrum Digital Soil Morphometrics, Springer, 73-87.
Summers, D., Lewis, M., Ostendorf, B., & Chittleborough, D. (2011). Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological. Indicators. 11: 123-131.
Vapnik, V. and Vapnik, V. (1998). Statistical learning theory. Wiley. New York. 156-160.
Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O. 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131: 59–75.
Walkley, A., & Black, I.A. (1934). An examination of the Degtjareff method for determining organic carbon in soils: effect of variations in digestion conditions and of inorganic soil constituents. Soil Science, 63: 251-263.
Wilding, L. (1985). Soil Spatial variability: Its documentation, accommodation, and implication to soil surveys. In Soil Spatial Variability. D.R. Nielson and J. Bouma (eds), Pudo, Wagenigen, the Netherlands, 166-194.
Wischmeier, W. H. & Smith, D.D. (1978). Predicting Rainfall Erosion Losses, a Guide to Conservation Planning. Agriculture Handbook No. 537. U.S. Department of Agriculture, Washington, DC.
Wold S., Sjostrom M. & Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratoary Systems, 58: 109–130.
Zhao, Z., Chow, T. L., Rees H. W., Yang, Q., Xing, Z., & Meng, F. (2009). Predict soil texture distributions using an artificial neural network model. Computers and Electronics Agrriculture, 65: 36-48.
Zhu, Y. M., Lu X. X., & Zhou Y. (2007). Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology, 84: 111–125.
Iranian Journal of Soil and Water Research
Volume 54, Issue 6
September 2023
Pages 915-931

Files

Share

How to cite

Statistics