Modeling Soil Salinity in Khuzestan Lands Susceptible for Dust Production Using Visible-Near Infrared Spectroscopic Method

Document Type : Research Paper


1 PhD Student, Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Professor, Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran, Dust research center, Shahid Chamran university of Ahvaz, Ahvaz, Iran

3 Associate Professor, Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

4 Associate Professor, Soil Conservation and Watershed Management Research Institute, Tehran, Iran

5 Associate Professor, Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran


A broad area of saline and semi-saline lands of Khuzestan province have changed into centers susceptible to dust production due to eroded wind and lack of surface coating and low soil resistance. The objective of this study was to model the soil salinity of sensitive areas to dust production in Khuzestan Provenience usin spectrometry method of visible and near-infrared wavelengths (2500-350 nm). The least square multivariate regression model, artificial neural network and random forest model were used to estimate soil salinity. The main soil spectrum was determined using the FieldSpect machine. Also, preprocessing methods including Savitzky-Golay filter, the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normalization method (SNV), and the continuum remove method (CR) were used to eliminate the noise and to increase the accuracy of the multivariate model. The results showed that the combined model partial least squares-artificial neural network model with assessment criteria (RPDcal = 3.40-2.65) has high accuracy for salinity estimation. In contrast, the combined model of least squares - random forest showed the lowest accuracy (RPDcal = 0.85-1.98). Preprocess of the main spectrum in two models (neural network and partial least squares regression) increased the relative accuracy of the model; while in the random forest model, preprocess reduced the accuracy of the model compared to the main spectrum. The ranges of 1800, 1900, 2000, 2300 and 1500 nm were recognized as "the key wavelengths" impressed by soil salinity. The key wavelengths can be used in remote sensing studies and mapping of soil salinity in areas sensitive to dust production in Khuzestan province.


Main Subjects

Breiman, L. (1999). Using adaptive bagging to debias regressions: Technical Report 547, Statistics Dept. UCB
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32
CAMO, A. (1998). The Unscrambler User Manual. CAMO ASA Norway
Caudill, M, (1987) Neural networks primer, part I. AI expert, 2(12), 46-52
Chang, C.-W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Science Society of America Journal, 65(2), 480-490
Chen, X.-W., & Liu, M. (2005). Prediction of protein–protein interactions using random decision forest framework. Bioinformatics, 21(24), 4394-4400
Clark, R.N., 1999. Chapter 1: Spectroscopy of Rocks and Minerals and Principles of Spectroscopy, Manual of Remote Sensing. (A.N. Rencz, ed.) John Wiley and Sons, New York, p 3-58, 1999. (Invited book chapter) Online at:
Curcio, D., Ciraolo, G., D’Asaro, F., & Minacapilli, M. (2013). Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences, 19, 494-503
Curran, P.J., Dungan, J.L., Peterson, D.L., 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry, testing the Kokaly and Clark methodologies. Rem. Sens. Environ. 76: 349–359.
Demuth, H., & Beale, M. (1998). Neural network toolbox: For use with MATLAB, Natick, MA: The Math Works. Inc.. OpenURL
Drake, N. (1995). Reflectance spectra of evaporite minerals (400-2500 nm): applications for remote sensing. International Journal of Remote Sensing, 16(14), 2555-2571.
Farifteh, J., Van der Meer, F., Atzberger, C., & Carranza, E. (2007). Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN). Remote Sensing of Environment, 110(1), 59-78
Farifteh, J., Van der Meer, F., Van der Meijde, M., & Atzberger, C. (2008). Spectral characteristics of salt-affected soils: A laboratory experiment. Geoderma, 145(3-4), 196-206
Fearn, T., Riccioli, C., Garrido-Varo, A., & Guerrero-Ginel, J. E. (2009). On the geometry of SNV and MSC. Chemometrics and Intelligent Laboratory Systems, 96(1), 22-26
Gomez, C., Lagacherie, P., & Coulouma, G. (2008). Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma, 148(2), 141-148
Hassani, A., Bahrami, H., Noroozi, A., & Oustan, S. (2014). Visible-near infrared reflectance spectroscopy for assessment of soil properties in gypseous and calcareous soils. Journal of Watershed Engineering and Managemen
He, T., Wang, J., Lin, Z., & Cheng, Y. (2009). Spectral features of soil organic matter. Geo-spatial Information Science, 12(1), 33-40
Huber, S., Kneubuhler, M., Psomas, A., Itten, K., Zimmermann, N.E. 2008. Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. For. Ecol. Manage, 256: 491-501.
Huang, Z., Turner, Brian, J., Dury, Stephen J., Wallis, Ian R. Foley, William, J. 2004. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment, 93(1): 18-29
Iran Meteorological Organization(
Ji, W., Adamchuk, V. I., Biswas, A., Dhawale, N. M., Sudarsan, B., Zhang, Y., . . . Shi, Z. (2016). Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields. biosystems engineering, 152, 14-27
Ji, W., Li, S., Chen, S., Shi, Z., Rossel, R. A. V., & Mouazen, A. M. (2016). Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions. Soil and Tillage Research, 155, 492-500
Khayamim, F., Khademi, H., Stenberg, B., & 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.
Kokaly, R.F. 2011. PRISM: Processing routines in IDL for spectroscopic measurements (installation manual and user’s guide, version 1.0): U.S. Geological Survey Open-File Report 2011–1155, 431p.
Mohamed, E., Saleh, A., Belal, A., & Gad, A. A. (2018). Application of near-infrared reflectance for quantitative assessment of soil properties. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 1-14
Nawar, S., Buddenbaum, H., & Hill, J. (2015). Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: a case study from Egypt. Arabian Journal of Geosciences, (7) 8, 5127-5140.
Nawar, S., Buddenbaum, H., Hill, J., & Kozak, J. (2014). Modeling and mapping of soil salinity with reflectance spectroscopy and landsat data using two quantitative methods (PLSR and MARS). Remote Sensing, 6(11), 10813-10834
Nawar, S.,Buddenbaum, H., Hill, J., Kozak, J., & Mouazen, A. M. (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
Pu, R., Ge, S., Kelly, N., & Gong, P. (2003). Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 24(9), 1799-1810
Rahmati, H., Gholizadeh, S., & Ansari, H. (2018). Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models. journal of geography and planning, 21(62), 95-115 (In Farsi).
Rossel, R. V., Cattle, S. R., Ortega, A., & Fouad, Y. (2009). In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma, 150(3-4), 253-266
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 (pp. 73-87): Springer
Sjöström, M., Wold, S., Lindberg, W., Persson, J.-Å., & Martens, H. (1983). A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables. Analytica Chimica Acta, 150, 61-70
Dehaan, R., & Taylor, G. (2002). Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization. Remote Sensing of Environment, 80(3), 406-417
Wang, J., Ding, J., Abulimiti, A., & 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, 6e4703
Wang, J., Li, Z., Qin, X., Yang, X., Gao, Z., & Qin, Q. (2014). Hyperspectral predicting model of soil salinity in Tianjin costal area using partial least square regression. Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Weng, Y., Gong, P., & Zhu, Z. (2008). Soil salt content estimation in the Yellow River delta with satellite hyperspectral data. Canadian Journal of Remote Sensing, 34(3), 259-270
Wenjun, J., Zhou, S., Jingyi, H., & Shuo, L. (2014). In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PloS one, 9(8), e105708
Xu, C., Zeng, W., Huang, J., Wu, J., & van Leeuwen, W. (2016). Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data. Remote Sensing, 8(1), 42
Xuemei, L., & Jianshe, L. (2013). Measurement of soil properties using visible and short wave-near infrared spectroscopy and multivariate calibration. Measurement, 46(10), 3808-3814
Zeng, W., Zhang, D., Fang, Y., Wu, J., & Huang, J. (2018). Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data. Journal of Applied Remote Sensing, 12(2), 022204