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Estimating Soil Salt Content Using Fractional Derivatives and Optional Spectral Indices in the Ebinur Lake Oasis, Northwestern China

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Data-Enabled Discovery and Applications

Abstract

Derivative spectroscopy is a powerful mathematical tool that provides more useful information of spectral data than untreated data. Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. The VIS-NIR spectrum is one of the most important data acquisition technologies for digital soil mapping, precision agriculture, and soil resource surveys. The objective of this study was to develop a soil salt content model using the fractional-order derivatives of field-measured spectral data paired with ground measurements. The results showed the following: There was a significant correlation between the single-band reflectance spectra and the soil salt content, and as the derivative order increased, reflectance values first increased and then decreased, with a peak value, R = 0.525, in the 1.2-order derivative. Strong correlations between the estimation of soil salt content and the derivative spectral data of the difference index (DI), ratio index (RI), and normalized difference index (NDI) were also produced by the 1.2-order derivative. R values for the DI, RI, and NDI were 0.818, 0.8624, and 0.8297, respectively. Selected bands with the highest correlation values from the 1.2-order derivative spectral data of the R1428, DI (R1426 / R2151), RI (R1429 − R2024), and NDI (R1526 − R2470)/(R1526 + R2470)] were used to build a model to estimate soil salt content, which showed an R2 value of 0.53 and low RMSE and SD values. The 15 samples of the data were then used to validate the single-band model, R1428, and the combination models, DI (R1426 / R2151), RI (R1429 − R2024), and NDI [(R1526 − R2470)/(R1526 + R2470)], which demonstrated that the R2 values were greater than 0.8. The RMSE and SD values were low, and the value of the RPD was greater than 1.4. This study demonstrated the potential of using fractional derivatives rather than integer derivatives for soil salt content estimation. By making full use of hyperspectral data, fractional derivatives could enrich data preprocessing methods and unearth information about spectral emissions that are lost by integer derivatives. Although this study is a straightforward application of the fractional derivative method, it provides a reference for the estimation of other parameters with hyperspectral technology.

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References

  1. K. Baderia, A. Kumar, G.K. Singh, Hybrid method for designing digital FIR filters based on fractional derivative constraints. ISA. Trans. 58, 493–508 (2015)

    Article  Google Scholar 

  2. V. de Paul Obade, L. Rattan, Assessing land cover and soil quality by remote sensing and geographical information systems (GIS). Catena 104, 77–92 (2013)

    Article  Google Scholar 

  3. J.L. Ding, D.L. Yu, Monitoring and evaluating spatial variability of soil salinity in dry and wetseasons in the Werigan-Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments. Geoderma 2014, 316–322 (2014)

    Article  Google Scholar 

  4. A. El Harti, R. Lhissoua, K. Chokmani, et al., Spatiotemporal monitoring of soil salinization in irrigated Tadla plain (Morocco) using satellite spectral indices. Int. J. Appl. Earth Obs. Geoinf. 50, 64–73 (2016)

    Article  Google Scholar 

  5. Y.C. Gao, J.N. Wang, S.H. Guo, Y.L. Hua, T.T. Li, et al., Effects of salinization and crude oil contamination on soil bacterial community structure in the Yellow River Delta region, China. Appl. Soil Ecol. 86, 165–173 (2015)

    Article  Google Scholar 

  6. R. Gerbbers, V.I. Adamchuk, Precision agriculture and food security. Science 327, 828–831 (2010)

    Article  Google Scholar 

  7. Hamzeh, A.A. Naseri, Behzad, S.K. Alavi Panah, Estimating salinity stress in sugarcane fields with spaceborne; hyperspectral vegetation indices. Int. J. Appl. Earth Obs. Geoinf. 21(1), 282–290 (2013)

    Article  Google Scholar 

  8. A. Harti, R. Lhissou, K. Chokmani, Spatiotemporal monitoring of soil salinization in irrigated Tadla ain (Morocco) using satellite spectral indices. Int. J. Appl. Earth Obs. Geoinf. 2016(50), 64–73 (2016)

    Article  Google Scholar 

  9. W. Ji, Z. Shi, Q. Zhou, L.Q. Zhou, VIS-NIR reflectance spectroscopy of the organic matter in several types of soil. J. Infrared Millim Waves 31(3), 277–282 (2012) (In Chinese)

    Article  Google Scholar 

  10. Jin X L, Du J, Liu H J, Wang Z M, Song K S, Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model. Agric For Meteorol 218–219, 250–260 (2006)

    Google Scholar 

  11. Z.X. Leng, L.M. Ge, X.L. Nurbey Pan, Functional district of Ebinur Lake watershed based on GIS. South North Water Transf. Water Sci. Technol. 4(1), 33–35 (2006)

    Google Scholar 

  12. Y. Li, H. Tang, H. Chen, Fractional-order derivative spectroscopy for resolving simulated overlapped Lorenztian peaks. Chemom. Intell. Lab. Syst. 107, 83–89 (2011)

    Article  Google Scholar 

  13. J. Li, L. Pu, M. Zhu, R. Zhang, The present situation and hot issues in the salt-affected soil research. Act Geograph. Sin. 67(9), 1233–1245 (2012) (In Chinese)

    Google Scholar 

  14. X. Li, Y. Zhang, Y. Bao, Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote. Sens. 6, 6221–6241 (2014)

    Article  Google Scholar 

  15. H.M. Ozaktas, B. Barshan, D. Mendlovic, Convolution and filtering in fractional Fourier domains. Opt. Rev. 1(1), 15–16 (1994)

    Article  Google Scholar 

  16. Y.I. Pankova, A.F. Novikova, A. Kontoboytseva, in Developments in Soil Salinity Assessment and Reclamation, ed. by S. A. Shahid, M. A. Abdelfattah, F. K. Taha. The new map of soil salinity and regularities in distribution of salt-affected soils in Russia (Springer-Verlag, Berlin, 2013), pp. 99–111

    Chapter  Google Scholar 

  17. J. Peng, H. Liu, Z. Shi, Regional heterogeneity of hyperspectral characteristics of salt-affected soil and salinity inversion. Trans. Chin. Soc. Agric. Eng. 30(17), 167–174 (2014) (In Chinese)

    Google Scholar 

  18. V. Poenaru, A. Badea, S.M. Cimpeanu, Multi-temporal multi-spectral and radar remote sensing for agricultural monitoring in the Braila plain. Agric. Agric. Sci. Proscenia 6, 506–516 (2015)

    Google Scholar 

  19. Y. Pu, W. Wang, J. Zhou, Y. Wang, H. Jia, Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation, Science in China. Series F. Inf. Sci. 51(9), 1319–1339 (2009)

    MATH  Google Scholar 

  20. F. Rui, Z. Yushu, W. Yu, Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress. Acta Ecol. Sin. 33, 301–307 (2013)

    Article  Google Scholar 

  21. P.A. Sanchez, S. Ahamed, F. Carre, et al., Digital soil map of the world. Science 325, 680–681 (2009)

    Article  Google Scholar 

  22. E. Scudiero, T.H. Skaggs, L. Dennis, Corwin. Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance. Ecol. Indic. 2016(70), 276–284 (2016)

    Article  Google Scholar 

  23. V.E. Tarasov, On chain rule for fractional derivatives communications in. Nonlinear Sci. Num. Simul. 30(1), 1–4 (2016)

    Google Scholar 

  24. S. Tonglu, L. Jianjun, H. Zhi, Tunable continuous-wave terahertz radiation system based on photomixing. Chin. J. Lasers. 41(4), 0411001 (2014)

    Article  Google Scholar 

  25. X. Wang, F. Zhang, K. Hsiang-Te, H. Yu, Spectral response characteristics and identification of typical plant species in Ebinur lake wetland national nature reserve (ELWNNR) under a water and salinity gradient. Ecol. Indic. 81, 222–234 (2017)

    Article  Google Scholar 

  26. J. Xu, F. Xinlu, G. Liang, et al., Fractional differential application in preprocessing infrared spectral data. Control Instrum. Chem. Ind. 39(3), 347–351 (2012)

    Google Scholar 

  27. I. Yahiaoui, A. Douaoui, Q. Zhang, A. Ziane, Soil salinity prediction in the lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis. J. Arid. Land 7, 794–805 (2015)

    Article  Google Scholar 

  28. G. Yang, L. Lu, C. He, et al., Baseline correction method for Raman spectra based on generalized Whittaker smoother. Chin. J. Lasers. 42(9), 0915003 (2015)

    Article  Google Scholar 

  29. F. Zhang, T. Tashpolat, V.C. Johnson, H. Kung, Evaluation of land desertification from 1990 to 2010 and its causes in Ebinur Lake region, Xinjiang China. Environ. Earth Sci. 73, 5731–5745 (2015)

    Article  Google Scholar 

  30. B. C. O’Kelly, Accurate determination of moisture content of organic soils using the oven drying method. Drying Technology 22(7), 1767–1776 (2004)

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Acknowledgments

The authors wish to thank the referees for providing helpful suggestions to improve this manuscript. The authors wish to thank Professor Abduwasit Ghulam (Saint Louis University) have helped us to improve the manuscript carefully. Please see the revised version of the paper.

Funding

The research was carried out with the financial support provided by the Xinjiang Local Outstanding Young Talent Cultivation Project of National Natural Science Foundation of China (U1503302).

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Authors and Affiliations

  1. Key Laboratory of Smart City and Environmental Modeling of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang, 830046, China

    Fei Zhang & Xiaoping Wang

  2. Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, Xinjiang, 830046, China

    Fei Zhang & Xiaoping Wang

  3. Department of Earth Sciences, The University of Memphis, Memphis, TN, 38152, USA

    Hsiang-te Kung

  4. Department of Physical and Environmental Sciences, Colorado Mesa University, Grand Junction, CO, 81501, USA

    Verner Carl Johnson

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  1. Fei Zhang
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  2. Xiaoping Wang
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Correspondence to Fei Zhang.

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Zhang, F., Wang, X., Kung, Ht. et al. Estimating Soil Salt Content Using Fractional Derivatives and Optional Spectral Indices in the Ebinur Lake Oasis, Northwestern China. Data-Enabled Discov. Appl. 2, 6 (2018). https://doi.org/10.1007/s41688-018-0017-2

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