Investigation of Absorbance Characteristics of Soil Organic carbon using Laboratory Spectroscopy in dust sensitive areas of Khuzestan Province

Document Type : Complete scientific research article

Authors

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

2 Shahid Chamran University of ahvaz

3 Conservation and Watershed Management Research Institute, Tehran, Iran

4 Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

Abstract

Background and Objectives: In recent years, due to the lack of surface coating and low soil resistance to wind erosion, the large area of Khuzestan province is sensitive to dust production. Among the soil characteristics, the organic matter by collecting soil particles, has important role in soil resistance to wind erosion and dust production. Since these areas are so wide, the use of traditional methods of soil analysis is really costly and time consuming. The spectroscopy approach, due to the advantage of speed and easy movement, can reduce the cost and time of measurement. The aim of this study is to investigate the spectral behavior of soil organic carbon in central and southern regions of Khuzestan province by using tow multivariate regression, Support Vector Regression) SVR( and neural network (PLS-ANN), and key wavelength determination of soil organic matter in these areas.
Materials and Methods: In this research, the study area was divided into 2 km square grids and systematic and random sampling Methods were performed. The soil organic matter in samples was measured in the laboratory. The Reflectance spectra of soil samples were determined using FildSpect setup in dark room. And spectral measurements were carried out with three types of detectors in range of visible to near infrared (3500-2500 nm). To eliminate the noise in normal reflectance spectra, the main spectra were preprocessed by four methods, including the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variant method (SNV) and the continuum removed method (CR). Next, the performance of SVR and PLS-ANN models in main spectra preprocessed method were compared.
Results: The results showed that the PLS-ANN model had better accuracy compared to SVR model in estimating organic carbon. In SVR models, the continuum removal method (CR) had the best performance (R2CAL=0.84, RMSECAL=0.06 and RPDCAL=1.82) and the main Spectra had the worst performance (R2CAL=0.74, RMSECAL=0.14 and RPDCAL=1.66). In PLS-ANN models, the best performance belonged to the second derivative (SD-SG), (R2CAL=0.92, RMSECAL=0.05 and RPDCAL=2.34) and the worst performance was related to the first derivative (FD-SG), (R2CAL=0.80, RMSECAL=0.1 and RPDCAL=1.86).
Conclusion: In this study, the preprocessing methods improved the overall accuracy of SVR and PLS-ANN models compared to the main spectrum. According to the second derivative method, in PLS-ANN witch had the best accuracy in estimating soil organic carbon, the Wavelength ranges around 800, 1800 and 2000 nm were identified as the key wavelength of the organic carbon in sensitive centers to dust production.

Keywords

References

1.Bartholomeus, H., Schaepman, M., Kooistra, L., Stevens, A., Hoogmoed, W., and Spaargaren, O. 2008. Spectral reflectance based indices for soilorganic carbon quantification. Geoderma, 145: 1-2. 28-36.
2.Baumgardner, M.F., Silva, L.F., Biehl, L.L., and Stoner, E.R. 1986. Reflectance properties of soils Advances in Agronomy, 38: 1-44.
3.CAMO, A. 1998. The Unscrambler User Manual. CAMO ASA Norway. Pp: 72-82.
4.Caudill, M. 1987. Neural networks primer, part I. AI expert, 2: 12. 46-52.5.Chang, C.W., Laird, D.A., Mausbach, M.J., and Hurburgh, C.R. 2001.
Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Sci. Soc. Amer. J. 65: 2. 480-490.
6.Clark, R.N. 1999. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual of Remote Sensing, 3: 3-
7.Clark, R.N., and Roush, T.L. 1984. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res. Solid Earth. 89: B7. 6329-6340.
8.Crowley, J.K. 1991. Visible and near‐infrared (0.4-2.5 μm) reflectance spectra of playa evaporite minerals. J. Geophys. Res. Solid Earth. 96: B10. 16231-16240.
9.Curran, P.J., Dungan, J.L., and Peterson, D.L. 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76: 3. 349-359.
10.Demuth, H., and Beale, M. 1998.Neural network toolbox: For use with MATLAB, Natick, MA: The Math Works. Inc, 14-
11.Dotto, A.C., Dalmolin, R.S.D., ten Caten, A., and Grunwald, S. 2018. A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma, 314: 262-274.
12.Farifteh, J., Van der Meer, F., Van der Meijde, M., and Atzberger, C. 2008. Spectral characteristics of salt-affected soils: A laboratory experiment. Geoderma, 145: 3-4. 196-206.
13.Fystro, G. 2002. The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods. Plant and soil, 246: 2. 139-149.
14.Gaffey, S., McFadden, L., Nash, D., and Pieters, C. 1993. Ultraviolet, visible and near-infrared reflectance spectroscopy: Laboratory spectra of geologic materials. Remote geochemical analysis: Elemental and Mineralogical Composition, 151: 43-77.
15.He, T., Wang, J., Lin, Z., and Cheng, Y. 2009. Spectral features of soil organic matter. Geo-spatial Information Science, 12: 1. 33-40.
16.Heidarian, P., Azhdari, A., Joudaki, M., Khatooni, J.D., and Firoozjaei, S.F. 2018. Integrating Remote Sensing, GIS, and Sedimentology Techniques for Identifying Dust Storm Sources: A Case Study in Khuzestan, Iran. J. Ind. Soc. Rem. Sens. 46: 7. 1113-1124.
17.Hong, Y., Chen, S., Liu, Y., Zhang, Y., Yu, L., Chen, Y., Liu, Y., Cheng, H., and Liu, Y. 2019. Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy. Catena, 174: 104-116.
18.Huang, Z., Turner, B.J., Dury,S.J., Wallis, I.R., and Foley, W.J.2004. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment, 93: 1-2. 18-29.
19.Ji, W., Adamchuk, V.I., Biswas, A., Dhawale, N.M., Sudarsan, B., Zhang, Y., Rossel, R.A.V., and Shi, Z. 2016. Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields. Biosystems Engineering, 152: 14-27.
20.Ji, W., Shi, Z., Huang, J., and Li, S. 2016. Correction: In Situ Measurement of Some Soil Properties in Paddy
Soil Using Visible and Near-Infrared Spectroscopy. PloS one,11: 7. P. e0159785.
21.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.
22.Kokaly, R.F. 2011. PRISM: Processing routines in IDL for spectroscopic measurements (installation manual
and user's guide, version 1.0): N0: 193. P. 432.
23.Kuang, B., and Mouazen, A. 2011. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms. Europ. J. Soil Sci. 62: 4. 629-636.
24.Le Guillou, F., Wetterlind, W., Rossel, R.V., Hicks, W., Grundy, M., and Tuomi, S. 2015. How does grinding affect the mid-infrared spectra of soil and their multivariate calibrations to texture and organic carbon? Soil Research, 53: 8. 913-921.
25.Lobell, D.B., and Asner, G.P. 2002. Moisture effects on soil reflectance. Soil Sci. Soc. Amer. J. 66: 3. 722-727.
26.Mohamed, E., Saleh, A., Belal, A., and Gad, A.A. 2018. Application of near-infrared reflectance for quantitative assessment of soil properties. The Egypt. J. Rem. Sens. Space Sci. 21: 1. 1-14.
27.Nawar, S., Buddenbaum, H., and Hill, J. 2015. Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: a case study from Egypt. Arabi. J. Geosci. 8: 7. 5127-5140.
28.Nawar, S., Buddenbaum, H., Hill, J., Kozak, J., and 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.
29.Nelson, D.W., and Sommers, L.E. 1982. Total carbon, organic carbon and organic matter: P 539-579. In: A.L. Page, R.H. Miller and D.R. Keeney. Methods of soil analysis. Part 2 Chemical and Microbiological Properties, Madison, WI.
30.Nocita, M., Stevens, A., Toth, G., Panagos, P., van Wesemael, B.,
and Montanarella, L. 2014. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biology and Biochemistry,68: 337-347.
31.Ostovari, Y., 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.
32.Powlson, D., Brookes, P., Whitmore, A., Goulding, K., and Hopkins, D. 2011. Soil organic matters. Europ. J. Soil Sci. 62: 1-62.
33.Rossel, R.V., and Behrens, T. 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158: 1-2. 46-54.
34.Rossel, R.V., Behrens, T., Ben-Dor, E., Brown, D., Demattê, J., Shepherd,K.D., Shi, Z., Stenberg, B., Stevens, A., and Adamchuk, V. 2016. A global spectral library to characterize the world's soil. Earth-Science Reviews, 155: 198-230.
35.Rossel, R.V., 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: 3-4. 253-266.
36.Rossel, R.V., Fouad, Y., and Walter,C. 2008. Using a digital camera to measure soil organic carbon and iron contents. biosystems engineering, 100: 2. 149-159.
37.Rossel, R.V., Walvoort, D., McBratney, A., Janik, L.J., and Skjemstad, J.2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131: 1. 59-75.
38.Shi, T., Chen, Y., Liu, H., Wang, J., and Wu, G. 2014. Soil organic carbon content estimation with laboratory- based visible–near-infrared reflectance spectroscopy: Feature selection. Applied Spectroscopy, 68: 8. 831-837.
39.Smola, A.J., and Schölkopf, B.2004. A tutorial on support vector regression. Statistics and Computing, 14: 3. 199-222.
40.Tian, Y., Zhang, J., Yao, X., Cao,W. and Zhu, Y. 2013. Laboratory assessment of three quantitative methods for estimating the organic matter content of soils in China based on visible/near-infrared reflectance spectra. Geoderma, 202: 161-170.
41.Tong, P., Du, Y., Zheng, K., Wu, T., and Wang, J. 2015. Improvement of NIR model by fractional order Savitzky–Golay derivation (FOSGD) coupled with wavelength selection. Chemometrics and Intelligent Laboratory Systems,143: 40-48.
42.Udelhoven, T., Emmerling, C., and Jarmer, T. 2003. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study. Plant and soil, 251: 2.  319-329.
43.Vapnik, V., and Vapnik, V.1998. Statistical learning theory Wiley. New York. Pp: 156-160.
44.Wang, J., 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, p.e4703.
45.Wang, J., He, T., Lv, C., Chen, Y., and Jian, W. 2010. Mapping soil organic matter based on land degradation spectral response units using Hyperion images. Inter. J. Appl. Earth Observ. Geoinfor. 12: 171-180.
46.Weng, Y., Gong, P., and Zhu, Z. 2008. Soil salt content estimation in the Yellow River delta with satellite hyperspectral data. Can. J. Rem. Sens. 34: 3. 259-270.
47.Wilding, L. 1985. Spatial variability: its documentation, accommodation and implication to soil surveys. Paper presented at the Soil spatial variability. Las Vegas NV, 30 November-1 December 1984 (pp. 166-194).
48.Xu, C., Zeng, W., Huang, J., Wu, J., and van Leeuwen, W. 2016. Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data. Remote Sensing, 8: 1. p. 42.
49.Xuemei, L., and Jianshe, L. 2013. Measurement of soil properties using visible and short wave-near infrared spectroscopy and multivariate calibration. Measurement, 46: 10. 3808-3814.
50.Zheng, K.Y., Zhang, X., Tong, P.J., Yao, Y., and Du, Y.P. 2015. Pretreating near infrared spectra with fractional order Savitzky–Golay differentiation (FOSGD). Chinese Chemical Letters, 26: 3. 293-296.
Journal of Soil Management and Sustainable Production
Volume 10, Issue 1
May 2020
Pages 65-81

Files

Share

How to cite

Statistics