Quantitative Analysis of Crops Chlorophyll in the Heihe River Basin by Hyperspectral Remote Sensing Image

  • Jingjing Peng
  • Qiang Liu
  • Jiahong Li
  • Qinhuo Liu
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 129)


The continuous fine spectrums obtained by hyperspectral remote sensing provide broad accessibility to precisely estimate physiological and biochemical parameters of ground vegetation. Chlorophyll is responsible for absorbing sunlight, so the real-time and accurate monitoring of chlorophyll becomes an important research field of hyperspectral remote sensing in precision agriculture. By utilizing a spectral index which is sensitive to the chlorophyll content(Chl), this paper tried to calculate the Chl profiles of the arid region in Heihe River Basin based on hyperspectral remote sensing images acquired by Operational Modular Imaging Spectrometer (OMIS) in WATER campaign[1]. In addition, an Absorption Depth Index(ADI) was proposed as an effective indicator of the emissive chlorophyll fluorescence (ChlF) based on the spectral characteristics of Solar Fraunhofer lines, and the ChlF and ADI profiles at 760nm band of each experiment site were worked out. The inversion result of Chl and ChlF were in good conformity with the actual situation, which would serve as important materials for the real-time monitoring of crop growth in the Heihe river basin.


chlorophyll content fluorescence hyperspectral remote sensing styling Heihe river basin 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Li, X., Ma, M.G., Wang, J.: Watershed Allied Telemetry Experimental Research. Journal of Geophysical Research 114, 22103 (2009), doi:10.1029/2008JD011590CrossRefGoogle Scholar
  2. 2.
    Blackburn, G.A., Milton, E.J.: Seasonal variations in the spectral reflectance of deciduous tree canopies. International Journal of Remote Sensing 16(4), 709–720 (1995)CrossRefGoogle Scholar
  3. 3.
    Curran, P.J.: Remote sensing of foliar chemistry. Remote Sensing of Environment 30(3), 271–278 (1989)CrossRefGoogle Scholar
  4. 4.
    Hinzman, L.D., Bauer, M.E., Daughtry, C.S.: Effects of nitrogen-fertilization on growth and reflectance characteristics of winter-wheat. Remote Sensing of Environment 19(1), 47–61 (1986)CrossRefGoogle Scholar
  5. 5.
    Morales, F., Belkhodja, R., Goulas, Y., Abadia, J., Moya, I.: Remote and near-contact chlorophyll fluorescence during photosynthetic induction in iron-deficient sugar beet leaves. Remote Sensing of Environment 69(2), 170–178 (1999)CrossRefGoogle Scholar
  6. 6.
    Krause, G., Weis, E.: Chlorophyll fluorescence and photosynthesis: the basics. Annual Review of Plant Biology 42(1), 313–349 (1991)CrossRefGoogle Scholar
  7. 7.
    Gamon, J., Filella, I., Peuelas, J.: The dynamic 531-nanometer reflectance signal: A survey of 20 angiosperm species. Photosynthetic Responses to the Environment, 172–177 (1993)Google Scholar
  8. 8.
    Zarco-Tejada, P., Miller, J., Noland, T., Mohammed, G., Sampson, P.: Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 39(7), 1491–1507 (2002)CrossRefGoogle Scholar
  9. 9.
    Grace, J., Nichol, C., Disney, M., Lewis, P., Quaife, T., Bowyer, P.: Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence. Global Change Biology 13(7), 1484–1497 (2007)CrossRefGoogle Scholar
  10. 10.
    Liu, L.Y., Zhang, Y.J., Wang, J.H., Zhao, C.J.: Research on passive detection of vegetation chlorophyll fluorescence. Imaging Spectroscopy Technology Research and Application Essays (2004)Google Scholar
  11. 11.
    Yan, C.Y., Liu, Q.: Model Analysis and Validation by Using Airborne Hyperspectral Remote Sensing Data to Extract Winter Wheat Canopy Chlorophyll Content. Remote Sensing Information 005, 8–14 (2009)Google Scholar
  12. 12.
    Jacquemoud, S., Baret, F.: PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment 34(2), 75–91 (1990)CrossRefGoogle Scholar
  13. 13.
    Verhoef, W.: Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment 16(2), 125–141 (1984)CrossRefGoogle Scholar
  14. 14.
    Kussk, A.: The hot spot of a uniform vegetation cover. J. Remote Sensing 3(4), 645 (1985)Google Scholar
  15. 15.
    Xie, D.H., Zhu, Q.J., Wang, J.D., Xu, K.: Analysis on the vertical distribution of biochemical parameters based on a 3D virtual corn canopy scene. Journal of Beijing Normal University (Natural Science) 43(003), 337–342 (2007)Google Scholar
  16. 16.
    Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L.: Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81(2-3), 416–426 (2002)CrossRefGoogle Scholar
  17. 17.
    Yan, C.Y., Niu, Z., Wang, J.H., Liu, L.Y., Huang, W.J.: The assessment of spectral indices applied in chlorophyll content retrieval and a modified crop canopy chlorophyll content retrieval model. Journal of Remote Sensing 9(006), 742–750 (2005)Google Scholar
  18. 18.
    Yan, C.Y.: Study on methods and models for vegetation biochemical information retrieval by remote sensing. Beijing: Institute of Remote Sensing Applications. Chinese Academy of Science (2004)Google Scholar
  19. 19.
    Liu, L.Y., Zhang, Y.J., Wang, J.H., Zhao, C.J.: Detecting photosynthesis fluorescence under natural sunlight based on Fraunhofer line. Journal of Remote Sensing 10(001), 130–137 (2006)Google Scholar
  20. 20.
    Cheng, Z.H., Liu, L.Y.: Estimating light-use efficiency by the separated Solar-induced chlorophyll fluorescence from canopy spectral data. Journal of Remote Sensing 14(002), 356–371 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Jingjing Peng
    • 1
  • Qiang Liu
    • 1
    • 2
  • Jiahong Li
    • 3
  • Qinhuo Liu
    • 1
  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing ApplicationsChinese Academy of Sciences and Beijing Normal UniversityBeijingChina
  2. 2.College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina
  3. 3.National Remote Sensing Center of ChinaBeijingChina

Personalised recommendations