Investigating the Efficiency of Visible-Near Infra-Red (NIR) Spectrometry to Estimate Selected Soil Properties in Semirom Area, Isfahan

آب و خاک

Article 9, Volume 36, Issue 2 - Serial Number 82, May and June 2022, Pages 283-300   

Document Type: Research Article

DOI: 10.22067/jsw.2022.75858.1150

Authors

F. Rahmati¹; S. Hojati^{* 1}; K. Rangzan²; A. Landi¹

¹Department of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Khuzestan, Iran

²Department of GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

Introduction
 Estimating soil properties on large scales using experimental methods requires specialized equipments and can be extremely time-consuming and expensive, especially when dealing with a high spatial sampling density. Soil Visible and Near-InfraRed (V-NIR) reflectance spectroscopy has proven to be a fast, cost-effective, non-destructive, environmental-friendly, repeatable, and reproducible analytical technique. V-NIR reflectance spectroscopy has been used for 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 objectives of this study were to estimate soil properties (carbonate calcium equivalent (CCE), electrical conductivity (EC), pH, and organic carbon (OC)) using visible near-infrared and short-wave Infrared (SWIR) reflectance spectroscopy (350-2500 nm). In this study, the best predictions of all the soil properties, model and pre-processing technique were also determined. The Partial Least Squares Regression (PLSR), Artificial Neural Network, Support Vector Machine Regression and Principal Component Regression (PCR) models were also compared to estimate soil properties.
Materials and Methods
 A total number of 200 surface soil samples (0-10 cm) were collected from the Semirom region (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan, Iran. The samples were air dried and passed through a 2 mm sieve, and using standard procedures soil properties were determined in the laboratory. Accordingly, soil pH and the EC contents of soil samples were determined in saturated pastes and extracts, respectively. The CCE content of the soils were measured using back titration, and the OC contents of the samples were measured using Walkley-Black method. The Reflectance spectra of all samples were measured using an ASD field spectrometer. The selection of the best model was done according to the value of the Ratio of Performance to Deviation (RPD), the coefficient of determination (R²), and the Root Mean Square Eerror (RMSE).
Results and Discussion
 Once the models were constructed using PLSR, ANN, SVMR and PCR approaches, descriptive analysis was carried out for each property, for the data measured in the laboratory. The parameters calculated for the properties were mean, coefficient of variation (CV), minimum and maximum, standard deviation and range. Coefficient of variation for the organic carbon, CCE, pH, and EC values were 21.7, 12.4, 1.34, and 28.74, respectively. Wilding (1985) proposed low, medium, and high variability for the CV values less than 15%, 15-35%, and greater than 35%, respectively. Accordingly, the organic carbon and EC of soils could be classified in the group with moderate variability. However, the calcium carbonate equivalent and pH are in the group with low variability. Since spectral data preprocessing has an effective role on improving the calibration, in order to perform spectral preprocessing, two first nodes at the first (350-400 nm) and the end (2450-2500 nm) of each spectrum were removed. In addition, two interruptions were eliminated, due to the change in the detector in the range of 900 to 1700 nm. Different preprocessing methods i.e., Standard Normal Variable (SNV) and First (FD) and Second Derivatives (SD) and Savitzky-Golay preprocessing techniques were performed on spectral data. Then, using PLSR, the cross‐validation method was used to evaluate soil properties calibration and validation. According to Stenberg (2002), for agricultural applications, The values of RPD greater than 2 indicate that the models provide precise predictions, the values of RPD between 1.5 and 2 are considered to be reasonably representative, and the values of RPD less than 1.5 indicate poor predictive performance. The results indicated the desirable capability of the PLSR method in estimating the EC (RPD > 2, R² = 0.94), CCE (RPD > 2, R² = 0.88), and OC (RPD > 2, R² = 0.89). The best results of the pH (RPD > 2, R² = 0.79) were estimated by the SVMR method. In this study the best methods of preprocessing techniques were First (FD) and Second Derivatives (SD) and Savitzky-Golay filter.
Conclusion
 In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil properties and showed its potential for substituting laboratory analyses. Moreover, spectroscopy could be considered as a simple, fast, and low-cost method in predicting soil properties. The PLSR model with First and Second derivatives and Savitzky-Golay pre-processing techniques seems to be more robust algorithm for estimating EC, OC, and CCE. The best results of the pH were estimated by the SVMR method with First and Second derivatives and Savitzky-Golay pre-processing techniques.

Keywords

Artificial Neural Network (ANN); Partial Least Squares Regression (PLSR); Principal Component Regression (PCR); Spectroscopy; Support Vector Machine Regression (SVMR)

References

1. Aldabaa A., Weindorf D.C., Chakraborty S., Sharma A., and Li B. 2015. Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma 34(46): 229–240. https://doi.org/10.1016/j.geoderma.2014.09.011.
2. Bilgili A., van Es H., Akbas F., and Durak A. 2010. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. Arid Environments 74(2): 229-238. https://doi.org./10.1016/j.jaridenv.2009.08.011.
3. Bouyoucos G.J. 1951. A recalibration of hydrometer method for making mechanical analysis of soil. Agronomy 43: 434- https://doi.org/10.2134/agronj1951.00021962004300090005x.
4. Clark R.N. 1999. Spectroscopy of rocks and minerals, principles of spectroscopy. John Wiley and Sons.
5. Gras J.P., Barthès B.G. Mahaut B., and Trupin. S. 2014. Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoil. Geoderma 215: 126–134. https://doi.org/10.1016/j.geoderma.2013.09.021.
6. Guerrero C., Viscarra Rossel R.A., and Mouazen A.M. 2010. Diffuse reflectance spectroscopy in soil science and land resource assessment. Geoderma 158: 1-2. https://doi.org/10.1016/j.geoderma.2010.05.008.
7. Hassani A., Bahrami H.A., Noroozi A.A., and Oustan Sh. 2014. Visible-near infrared reflectance spectroscopy for assessment of soil properties in gypseous and calcareous soils. Watershed Engineering and Management 6(2): 125-138. (In Persian with English abstract) https://doi.org/10.22092/IJWMSE.2014.101721.
8. Hong Y., Chen S., Zhang Y., Chen Y., Yu L., Liu Y., and Cheng H. 2018. Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: Effects of two-dimensional correlation coefficient and extreme learning machine. Science of the Total Environment 644: 1232–1243. https://doi.org/10.1016/j.scitotenv.2018.06.319.
9. Islam K., Singh B., and McBratney A. 2003. Simultaneous estimation of several soil properties by ultraviolet, visible and nearinfrared reflectance spectroscopy. Australian Journal of Soil Research 41: 1193–1202. https://doi.org/10.1071/SR02137.
10. Jalalian A. 1997. The studies of lands resources and capability determination in Semirom area. The Ministry of Jahad Sazandegi, Isfahan Province. (In Persian)
11. Janik L.J., Forrester S.T., and 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. https://doi.org/10.1016/j.chemolab.2009.04.005.
12. Khayamim, Khademi H., Stenberg B., and Wetterlind J. 2015. Capability of vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province. Isfahan University of Technology 19(72): 81-92. (In Persian with English abstract). https://doi.org/10.18869/acadpub.jstnar.19.72.8.
13. Knadel M., Deng F., Alinejadian A., de Jonge L.W., Møldrup P., and Greve M.H. 2013. Moisture effects on visible-near infrared soil spectra-from wet to hyper dry. P. 422-433. In ASA, CSSA, and SSSA, -6 Nov. 2013. International Annual Meetings, Florida, United States of America. https://doi.org/10.2136/sssaj2012.0401.
14. Kuśnierek 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. p. 100-103. The 2^th Global Workshop on Proximal Soil Sensing, 15-18 May. 2011. Montreal, Canada.
15. Lanyon L.E., and 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, WI. https://doi.org/10.2134/agronmonogr9.2.2ed.c14.
16. Minasny B., and McBratney A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences 32(9): 1378-1388. https://doi.org/10.1016/j.cageo.2005.12.009.
17. Nawar, Buddenbaum H., Hill J., Kozak J., and 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. https://doi.org/10.1016/j.still.2015.07.021.
18. Ostovari, Ghorbani-Dashtaki S., Bahrami H.A., Abbasi M., Dematte J.A.M., Arthur, E., and Panagos P. 2018. Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran. Geoderma 314: 102-112. https://doi.org/10.1016/j.geoderma.2017.11.014.
19. 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 with English abstract). https://doi.org/10.22092/IJSR.2018.117044.
20. Reeves, McCarty G., and Mimmo T. 2002. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental Pollution 116: 277–284. https://doi.org/10.1016/S0269-7491(01)00259-7.
21. Reeves, and Smith D.B. 2009. The potential of mid-and near-infrared diffuse reflectance spectroscopy for determining major-and trace-element concentrations in soils from a geochemical survey of North America. Applied Geochemistry 24(8): 1472-1481. https://doi.org/10.1016/j.apgeochem.2009.04.017.
22. Richards L.A. 1954. Diagnosis and Improvement of Saline-Alkali Soils. US Department of Agriculture, Washington DC.
23. SargentJ. 2001. Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer 91: 1636-42. https://doi.org/10.1002/1097-0142(20010415)91:8+<1636::AID-CNCR1176>3.0.CO;2-D.
24. Savitzky, and Golay M.J. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36: 1627-1639. https://doi.org/10.1021/ac60214a047.
25. Seifi, Ahmadi A., Neyshabouri M.R., Taghizadeh-Mehrjardi R., and Bahrami H.A. 2020. Remote and Vis-NIR spectra sensing potential for soil salinization estimation in the eastern coast of Urmia hyper saline lake, Iran. Remote Sensing Applications: Society and Environment 20: 100398. https://doi.org/10.1016/j.rsase.2020.100398.
26. Shiferaw, and Hergarten Ch. 2014. Visible near infra-red (Vis-NIR) spectroscopy for predicting soil organic carbon in Ethiopia. Ecology and Natural Environent 6: 126-139. https://doi.org/10.5897/JENE2013.0374.
27. Summers, Lewis M., Ostendorf B., and Chittleborough D. 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological. Indicators 11: 123-131. https://doi.org/10.1016/j.ecolind.2009.05.001.
28. Stenberg B., Jonsson A., and Börjesson T. 2002. Near infrared technology for soil analysis with implications for precision agriculture. In Near Infrared Spectroscopy. p. 279-284. Proceedings of the 10^th International Conference. NIR Publications, 2002. Chichester, UK, Kyongju S. KA.
29. TerraS., Demattê J.A.M., and Viscarra Rossel R.A. 2015. Spectral libraries for quantitative analyses of tropical Brazilian soils: comparing Vis–NIR and mid-IR reflectance data. Geoderma 255–256: 81–93. https://doi.org/10.1016/j.geoderma.2015.04.017.
30. Vapnik, and Vapnik V. 1998. Statistical learning theory. John Wiley and Sons.
31. Viscarra RosselA., McGlynn R., and McBratney A. 2006. Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma 137(1-2): 70-82. https://doi.org/10.1016/j.geoderma.2006.07.004.
32. Viscarra Rossel R.A., Walvoort D.J.J., McBratney A.B., Janik L.J., and Skjemstad J.O. 2006. Visible, near infrared, midinfrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131: 59–75. https://doi.org/10.1016/j.geoderma.2005.03.007.
33. Viscarra Rossel R.A., Cattle S.R., Ortega A., and Fouad Y. 2009. In situ measurements of soil colour, mineral composition and clay content by Vis–NIR spectroscopy. Geoderma 150: 253–266. https://doi.org/10.1016/j.geoderma.2009.01.025.
34. Viscarra Rossel R., and Behrens T. 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158: 46–54. https://doi.org/10.1016/j.geoderma.2009.12.025.
35. Walkley A., and 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. https://doi.org/10.1097/00010694-194704000-00001.
36. Wang , Ding J., Abulimiti A., and Cai L. 2018. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China. PeerJ, 6: e4703. https://doi.org/10.7717/peerj.4703.
37. Wilding 1985. Soil Spatial variability: Its documentation, accommodation, and implication to soil surveys. p. 166-194. In Soil Spatial Variability. D.R. Nielson and J. Bouma (ed.), Pudo. 1985. Wagenigen,NL.
38. Wold, Sjostrom M., and Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratoary Systems 58: 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1.