Visible-Near Infrared (VIS-NIR) Spectrophotometry in Predicting Soil Particle Percentage Using Artificial Neural Network and Partial Least Squares Regression

Document Type : Research Article

Authors

• E. Mehrabi Gohari ¹
• H.R. Matinfar ²
• Ruhollah Taghizadeh-Mehrjardi
• A. Jafari ³

¹ Payam Noor University

² Lorestan University

³ Kerman

https://doi.org/10.22067/.v34i2.80806

Abstract

Introduction: Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and digital mapping of soil and on the other hand, soils are temporally and spatially variable, thus distinguish zoning and their monitoring with traditional sampling methods and laboratory analysis is very costly and time consuming. As a result, the development of methods for analyzing the soil and for required information has become very important. Visible and near infrared spectroscopy (VIS-NIR) is widely used to estimate soil physical properties and estimate soil texture. The present study aims to predict soil texture using spectral measurements and artificial neural network models and partial least squares regression.
Materials and Methods: The study area in southeastern Iran is approximately 70 km from Kerman. In the study area, based on the hypercube technique, 115 profiles were identified and then horizons were sampled. In this way, for each point of study, the necessary information, including the location of the profile on the ground, the type of geomorphic unit and the type of materiel, were recorded and taken from the horizons of each profile. In all soil samples, after drying and passing through 2 mm soil, the soil texture was measured by hypercube. Spectral radiometer was used to measure the spectral reflection of soil samples. The soil samples were air dried and sieved and then placed in a petri dish with an approximate diameter of 10 cm and transferred to the dark room for spectral analysis. Each specimen was tested four times (for each 90 degree sequential rotation) to remove the effects of a change in the radiation geometry. Soil samples were scanned, and absolute reflections at a spectral range of 2500-350 nm yielded 2150 spectral data points (SDPs) per soil sample with a spectral resolution of one nanometer. Finally, to construct a suitable model for forecasting the percentage of clay, sand, and silt, the least squares model was used with the number of factors 1 to 10 by Artificial Neural Network (ANN) modeling using JMP software Work.
Results and Discussion: The reflectance spectrum of the visible range - near infrared - was measured for specimens. Since preprocessing of spectral data has an effective role in improving the calibration, in order to perform spectral preprocessing, two first nodes of the first and the end of the spectra were first removed in the range of 350-400 and 2450-2500 nm. In addition, the interruption due to the change in the detector in the range of 900 to 1000 nm was also eliminated. Types of preprocessing methods were performed on spectral data. Then, using partial least squares regression analysis, the best model was produced when the first derivative was fitted to reflection values. The explanation coefficients for this low and unacceptable model were obtained. Therefore, using partial least squares regression analysis, the best wavelengths were selected to predict the percentage of clay, sand, soil, and extracted from the model. Then it was used as input in the neural network model. To determine the best combination, root error index and error coefficient were used. The results of artificial neural network showed that the number of neurons 9.8 and 10 had the best composition for predicting clay, sand and soil silt. The root-squared error results for clay, sand, and soil silt were 3.42, 6.94, and 4.383 respectively. Also, the results of the explanatory factor were 0.84, 0.83 and 0.81, respectively. After obtaining the optimal structure in the artificial neural network training phase described above, the trained network has been tested on the test data to determine the accuracy of this model to predict clay, sand and silt of surface soil. The root-squared error results for clay, sand and silt components were obtained at 5.54.9.14 and 7.01. Also, the results of the explanatory factor were 0.76.0.70 and 0.73 respectively. The best result of the prediction for partial least squares regression was obtained for the sand sample. The results indicate that the neural network performance is better than partial least squares regression, which is consistent with Mouazenet. al (2010) and also ViscarraRossel R. et. al (2009). Acceptable performance of the artificial-neural network can be attributed to the ability of this model for non-linear behavior of soil texture in visible spectroscopy. In this study, specific wavelengths, which Ben Finder et al. (2003) obtained in the study on the soils of Israel, were used. This conclusion confirms that various types of soil can be modeled using specific wavelengths. The advantage of this study is that, when using the artificial neural network, no pre-processing of reflection data is required before applying the model. Since the relationship between the percentage of soil particles (clay and gravel) and the reflection of the soil is not linear, the neural network method is very useful for analyzing the relationship between soils. Finally, the map of clay, sand and silt and map of soil texture was prepared by artificial neural network method in GIS environment.
Conclusion: The results of this study showed that the neural-dynamic network has a better performance than partial least squares regression. Calibration models designed and used in this study can be transported for use with other soils. When the partial least squares regression model was implemented, it had a very low accuracy (R2 ~ 0.1-0.3); on the contrary, the neural network-based method had high accuracy and less error. Note that although neural-dynamic modeling estimates higher precision results from soil texture, both approaches depend on wavelength selections, and so wavelengths should be selected before using any of the two models. To be finally, a meaningful relationship between the selected wavelengths and the percentage of clay, sand and silt in the present study indicates that soil texture is not only possible but also reliable by reflection spectroscopy.
 

Keywords

• Artificial neural network
• Infrared
• Modeling
• prediction
• Partial least squares regression
• Visible spectroscopy

References

1- Bellinaso H., Alexandre J., Dematte J.A.M., and Romeiro S.A. 2010. Soil spectral library and its use in soil classification. R. Bras. Ci. Solo 34: 861-870.

2- Ben-Dor E., Goldlshleger N., Benyamini Y., Blumberg D.G., and Agassi M. 2003. “The spectral reflectance properties of soil structural crusts in the 1.2- to 2.5-μm spectral region,” Soil Science Society of America Journal 67(1): 289–299.

3- Ben-Dor E., Taylor R.G., Hill J., Dematte J.A.M., Whiting M.L., Chabrillat S., and Sommer S. Imaging Spectrometry for Soil Applications, Advances in Agronomy, Vol.97, No. 2008, Elsevier Inc.

4- Clark R.N. 1999. Spectroscopy of rocks and minerals, and principles of spectroscopy. In: Rencz, A.N. (Ed.), Remote Sensing for Earth Sciences. Manual of Remote Sensing. John Wiley and Sons, Inc., Toronto, pp. 3–58.

5- Greve M.H., Kheir R.B., Greve M.B., and Bocher P.K. 2012. Quantifying the Ability of Environmental Parameters to Predict Soil Texture Fractions Using Regression-Tree Model with GIS and LIDAR Data: The Case Study of Denmark, Ecological Indicators 18: 1—10.

6- Gholizadeh A.A., Soom M.A.M., Saberioon M.M., and Boruvka L. 2013. Visible and near infrared reflectance spectroscopy to determine chemical properties of paddy soils. J. Food Agric. Environ 11: 859-866.

7- Gomez C., Lebissonnais Y., Annabi M., Bahri H., and Raclot D. 2013. Laboratory Vis—NIR Spectroscopy as an Alternative Method for Estimating the Soil Aggregate Stability Indexes of Mediterranean Soils, Geoderma 209-210: 86-97.

8- Hartemink A.E., and Minasny B. 2014. Towards Digital Soil Morphometrics, Geoderma 230231: 305-317.

9- Hunt G.R. 1977. Spectral signatures of particulate minerals in visible and near-infrared. Trans. Am. Geophys. Union 58: 553.

10- 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. Chemometr. Intell. Lab., 97: 179-188.

11- Khayamim F., Khademi H., Stenberg B., and Wetterlind J. 2015. Capability of vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province. JWSS - Isfahan University of Technology 19(72) :81-92.

12- Kodaira M., and Shibusawa S. 2013. Using a mobile real-time soil visible-near infrared sensor for high resolution soil property mapping. Geoderma 199: 64-79.

13- Kuang B., Mahmood H.S., Quraishi M.Z., Hoogmoed W.B., Mouazen A.M., and Van Henten E.J. 2012. Sensing Soil Properties in the Laboratory, In Situ, and On-Line: A Review: Advances in Agronomy, Elsevie Inc, Vol. 114.

14- Li D., Durand M., and Margulis S.A. 2011. Potential for Hydrologic Characterization of Deep Mountain Snowpack via Passive 2012. in the Tropics, Earth-Science Reviews 106: 52-62.

15- Mouazen A.M., Kuang B., DE Baerdemaeker and 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

16- Ramadan Z., Hopke P.K., Johnson M.J., and Scow K.M. 2005. Application of PLS and Back-Propagation Neural Networks for the estimation of soil properties. Chemometr. Intell. Lab., 75: 23-30.

17- Savitzky A., and Golay M.J. 1964. Smoothing and differentiation of data by simplified least squares.

18- Schaap M.G., Leij F.J., and van Genuchten M.Th. 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Journal Soil Sci. Soc. Am 62: 847–855.

19- Shepherd K.D., and Walsh M.G. 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J. 66: 988–998.

20- Soriano-Disla J.M., Janik L.J., Viscarra-Rossel R.A., Macdonald L.M., and Mclaughlin M.J. 2014. The Performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl. Spectrosc. Rev., 49: 139-186.

21- Stenberg B., Viscarra-Rossel R.A., Mouazen A.M., and Wetterlind J. 2010. Visible and near infrared spectroscopy in soil science. Adv. Agron., 107: 163-215.

22- Summers D., Lewis M., Ostendorf B., and Chittleborough D. 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecol. Indic., 11: 123-131.

23- Vasquez G.M., Grunwald S., and Sickman J.O. 2008. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146: 14-25.

24- Viscarra Rossel R., Cattle S.R., Ortega A., and Y. 2009. Fouad. In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy, Geoderma 150: 253–266.

25- Weather data of Zarand, Kerman province, 2015.

26- Zhao S.J., Zhang J., XU Y.M., and Xiong Z.H. 2006. Non-linear projections to latent structures method and its applications. Indian Eng. Chem. Res., 45: 3843-3852.

27- Zhao Z., Chow T.L., Rees H.W., Yang Q., Xing Z., and Meng F. 2009. Predict soil texture distributions using an artificial neural network model. Com. Elec. Agr, 65: 36-48.

28- Zhu Y., David C.W., and Zhang W. 2011. Characterizing Soils Using a Portable X-ray Fluorescence Spectrometer-1. Soil Texture, Geoderma 167-168: 167-177.29.

29- Zhu Y.M., Lu X.X., and 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.