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Journal of Bionic Engineering

, Volume 15, Issue 4, pp 741–750 | Cite as

Biomimetic Material Simulating Solar Spectrum Reflection Characteristics of Yellow Leaf

  • Kai Xu
  • Yang Lin
  • Xuezhong Wei
  • Hong Ye
Article
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Abstract

To counter the threat of hyperspectral detection, it is necessary to develop biomimetic materials to simulate the solar spectral reflection characteristics of plant leaf accurately. Two kinds of membranaceous yellow biomimetic materials were prepared by dispersing the particles of chrome titanium yellow and iron oxide yellow as fillers in polyvinyl alcohol films respectively. Reflectance and transmittance of the biomimetic materials were measured, and absorption and scattering coefficients of the biomimetic materials were inverted with a four-flux model. Results indicate that the biomimetic material adopting chrome titanium yellow particles can simulate the solar spectrum reflection characteristics of yellow leaf because of the similar absorption and scattering characteristics. The biomimetic material adopting iron oxide yellow particles cannot simulate the spectrum reflection characteristics of yellow leaf near the wavelength of 900 nm due to the characteristic absorption of the iron oxide. When the volume fraction of the chrome titanium yellow particles is lower than 2.12%, the absorption and scattering coefficients both increase approximately linearly with the volume fraction, indicating that the particles can scatter radiation independently. Therefore, the reflectance of the biomimetic material can be regulated through linearly changing of the volume fraction of the chrome titanium yellow particles.

Keywords

biomimetic material yellow leaf solar spectrum reflection four-flux model absorption coefficient scattering coefficient 
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Notes

Acknowledgment

This work was funded by the National Natural Science Foundation (No. 51576188).

References

  1. [1]
    Khan M J, Khan H S, Yousaf A, Khurshid K, Abbas A. Modern trends in hyperspectral image analysis: A review. IEEE Access, 2018, 6, 14118–14129.CrossRefGoogle Scholar
  2. [2]
    Shaw G, Manolakis D. Signal processing for hyperspectral image exploitation. IEEE Signal Processing Magazine, 2002, 19, 12–16.CrossRefGoogle Scholar
  3. [3]
    Chiang S S, Chang C I, Ginsberg I W. Unsupervised target detection in hyperspectral images using projection pursuit. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39, 1380–1391.CrossRefGoogle Scholar
  4. [4]
    Yuen P W, Richardson M. An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition. The Imaging Science Journal, 2010, 58, 241–253.CrossRefGoogle Scholar
  5. [5]
    Zhang H, Zhang J C. Near-infrared green camouflage of cotton fabrics using vat dyes. The Journal of The Textile Institute, 2008, 99, 83–88.CrossRefGoogle Scholar
  6. [6]
    Yang Y, Liu Z, Hu B, Man Y, Wu W. Bionic composite material simulating the optical spectra of plant leaves. Journal of Bionic Engineering, 2010, 7, S43–S49.CrossRefGoogle Scholar
  7. [7]
    Qin R, Xu G, Guo L, Jiang Y, Ding R. Preparation and characterization of a novel poly(urea-formaldehyde) microcapsules with similar reflectance spectrum to leaves in the UV-Vis-NIR region of 300–2500 nm. Materials Chemistry and Physics, 2012, 136, 737–743.CrossRefGoogle Scholar
  8. [8]
    Yuan Z, Ye H, Li S M. Bionic leaf simulating the thermal effect of natural leaf transpiration. Journal of Bionic Engineering, 2014, 11, 90–97.CrossRefGoogle Scholar
  9. [9]
    Yang X, Tang J, Mustard J F, Wu J, Zhao K, Serbin S, Lee J E. Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests. Remote Sensing of Environment, 2016, 179, 1–12.CrossRefGoogle Scholar
  10. [10]
    Ye H, Gao Y, Li S M, Guo L. Bionic leaves imitating the transpiration and solar spectrum reflection characteristics of natural leaves. Journal of Bionic Engineering, 2015, 12, 109–116.CrossRefGoogle Scholar
  11. [11]
    Demarez V. Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model. International Journal of Remote Sensing, 1999, 20, 879–894.CrossRefGoogle Scholar
  12. [12]
    Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81, 337–354.CrossRefGoogle Scholar
  13. [13]
    Wang L, Habibi M, Eldridge J I, Guo S M. Infrared radiative properties of plasma-sprayed BaZrO3 coatings. Journal of the European Ceramic Society, 2014, 34, 3941–3949.CrossRefGoogle Scholar
  14. [14]
    Maheu B, Letoulouzan J N, Gouesbet G. Four-flux models to solve the scattering transfer equation in terms of Lorenz-Mie parameters. Applied Optics, 1984, 23, 3353–3362.CrossRefGoogle Scholar
  15. [15]
    Xu Y, Ma H, Peng S. Study on identification of altered rock in hyperspectral imagery using spectrum of field object. Ore Geology Reviews, 2014, 56, 584–595.CrossRefGoogle Scholar
  16. [16]
    Viscarra Rossel R, Bui E, De Caritat P, McKenzie N. Mapping iron oxides and the color of Australian soil using visible- near-infrared reflectance spectra. Journal of Geophysical Research: Earth Surface, 2010, 115, F04031.CrossRefGoogle Scholar
  17. [17]
    Gao Y, Ye H. Bionic membrane simulating solar spectrum reflection characteristics of natural leaf. International Journal of Heat and Mass Transfer, 2017, 114, 115–124.CrossRefGoogle Scholar
  18. [18]
    Gates D M, Keegan H J, Schleter J C, Weidner V R. Spectral properties of plants. Applied Optics, 1965, 4, 11–20.CrossRefGoogle Scholar
  19. [19]
    Randrianalisoa J, Baillis D. Analytical model of radiative properties of packed beds and dispersed media. International Journal of Heat and Mass Transfer, 2014, 70, 264–275.CrossRefGoogle Scholar
  20. [20]
    Gunde M K, Orel Z C. Absorption and scattering of light by pigment particles in solar-absorbing paints. Applied Optics, 2000, 39, 622–628.CrossRefGoogle Scholar
  21. [21]
    Molenaar R, ten Bosch J J, Zijp J R. Determination of Kubelka–Munk scattering and absorption coefficients by diffuse illumination. Applied Optics, 1999, 38, 2068–2077.CrossRefGoogle Scholar
  22. [22]
    Cai Q, Ye H, Lin Q. Analysis of the optical and thermal properties of transparent insulating materials containing gas bubbles. Applied Thermal Engineering, 2016, 100, 468–477.CrossRefGoogle Scholar

Copyright information

© Jilin University 2018

Authors and Affiliations

  1. 1.Department of Thermal Science and Energy EngineeringUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Beijing Institute of Space Launch TechnologyBeijingChina

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