Volume 3, Issue 3

Original research papers

Biophysics

COMPARATIVE ANALYSIS OF HYPERSPECTRAL VEGETATION INDICES FOR REMOTE ESTIMATION OF LEAF CHLOROPHYLL CONTENT AND PLANT STATUS

Kalinka Velichkova, Dora Krezhova

Pages: 202–208

DOI: 10.21175/RadJ.2018.03.034

Received: 15 JUN 2018, Received revised: 9 DEC 2018, Accepted: 16 DEC 2018, Published online: 28 FEB 2019

Leaf chlorophyll (Chl) content, at the leaf and canopy level, is an important biochemical parameter because of its crucial role in photosynthesis and in plant functioning. Furthermore, it provides an indication of the plant nutritional state and stress. Due to the reliable, rapid, and non-destructive advantages, hyperspectral remote sensing plays a significant role in monitoring and assessing the plant biophysical variables. In this study, a set of Chl-related vegetation indices (VIs) derived from the leaf reflectance data of young pepper plants infected by Cucumber Mosaic Virus (CMV) were tested for estimating the changes in the Chl content and plant status. Hyperspectral reflectance data were collected by means of a portable fiber-optics spectrometer in the spectral range of 350-1100 nm. The effect of two growth regulators, MEIA (beta-monomethyl ester of itaconic acid) and ВТН (benzo(1,2,3)thiadiazole-7-carbothioic acid-S-methyl ester), on the Chl content and respectively on the development of the viral infection was investigated too. Four categories VIs: normalized difference (ND) VIs; simple ratio (SR) VIs; single-band reflectance or simple difference (SD) VIs, and some other forms of VIs, were tested using statistical analyses (ANOVA and Tukey-Kramer’s tests) to explore their potentials in the Chl content estimation. To enhance the sensitivity of the VIs, modified VIs were tested in some other combinations of narrow bands. The statistical analyses showed that the Modified Red Edge Simple Ratio (MRESR) index, Vogelmann Red Edge index (VREI1), and Pigment index (PI) were most sensitive to the Chl content changes. The Normalized Difference VI (NDVI) and Triangular Vegetation Index (TVI) turned out to be insensitive to Chl variations. The rest of the VIs were responsible for Chl variations but with less sensitivity.
  1. F. Fiorani, U. Schurr, “Future Scenarios for Plant Phenotyping,” Annu. Rev. Plant. Biol., vol. 64, pp. 267 – 291, Apr. 2013.
    DOI: 10.1146/annurev-arplant-050312-120137
    PMid: 23451789
  2. E. Levizou, P. Drilias, G. K. Psaras, Y. Manetas, “Nondestructive assessment of leaf chemistry and physiology through spectral reflectance measurements may be misleading when changes in trichome density co-occur,” New Phytol., vol. 165, no. 2, pp. 463 – 472, Feb. 2005.
    DOI: 10.1111/j.1469-8137.2004.01250.x
    PMid: 15720657
  3. D. A. Sims, J. A. Gamon, “Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages,” Remote Sens. Environ., vol. 81, no. 2-3, pp. 337 – 354, Aug. 2002.
    DOI: 10.1016/S0034-4257(02)00010-X
  4. A. P. Leone, N. Leone, S. Rampone, “An application of VIS-NIR reflectance spectroscopy and artificial neural networks to the prediction of soil organic carbon content in Southern Italy,” Fresen. Environ. Bull., vol. 22, no. 4b, pp. 1225 – 1229, Apr. 2013.
    Retrieved from: http://xoomer.virgilio.it/rampon/visnir20132.pdf;
    Retrieved on: Feb. 12, 2018
  5. C. Zhang, I. Filella, M. F. Garbulsky, J. Peñuelas, “Affecting factors and recent improvements of the photochemical reflectance index (PRI) for remotely sensing foliar, canopy and ecosystemic radiation-use efficiencies,” Remote Sens., vol. 8, no. 9, pp. 677 – 709, Sep. 2016.
    DOI: 10.3390/rs8090677
  6. Y. C. Tian et al., “Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space- based hyperspectral reflectance,” Field Crops Res., vol. 120, no. 2, pp. 299 – 310, Jan. 2011.
    DOI: 10.1016/j.fcr.2010.11.002
  7. A. J. S. Neto, D. de Carvalho Lopes, J. C. F. Borges Júnior, “Assessment of Photosynthetic Pigment and Water Contents in Intact Sunflower Plants from Spectral Indices,” Agriculture, vol. 7, no. 2, pp. 8 – 16, Feb. 2017.
    DOI: 10.3390/agriculture7020008
  8. S. Lu et al., “Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces,” J. Exp. Bot., vol. 66, no. 18, pp. 5625 – 5637, Sep. 2015.
    DOI: 10.1093/jxb/erv270
    PMid: 26034132
    PMCid: PMC4585420
  9. P. J. Zarco-Tejada et al., “A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index,” Remote Sens. Environ., vol. 138, pp. 38 – 50, Nov. 2013.
    DOI: 10.1016/j.rse.2013.07.024
  10. D. Krezhova, S. Maneva, N. Petrov, K. Velichkova, „Remote sensing of the influence of the biotic stress on plant biophysical variables,” Radiation & Applications, vol. 2, no. 1, pp. 46 – 52, Apr. 2017.
    DOI: 10.21175/RadJ.2017.01.010
  11. E. R. Hunt, P. C. Doraiswamy, J. E. McMurtrey, C. S. T. Daughtry, E. M. Perry, “A Visible Band Index for Remote Sensing Leaf Chlorophyll Content at the Canopy Scale,” Int. J. Appl. Earth Obs. Geoinf., vol. 21, pp. 103 – 112, Apr. 2013.
    DOI: 10.1016/j.jag.2012.07.020
  12. N. Petrov, M. Stoyanova, M. Valkova, “Antiviral activity of plant extract from Tanacetum vulgare against Cucumber Mosaic Virus and Potato Virus Y,” J. BioSci. Biotechnol, vol. 5, no. 2, pp. 189 – 194, Jul. 2016.
    Retrieved from: http://www.jbb.uni-plovdiv.bg/documents/27807/1703628/2016-5-2-189-194.pdf;
    Retrieved on: Mar. 12, 2018
  13. J. Rouse, R. Haas, J. Schell, D. Deering, Monitoring Vegetation Systems in the Great Plains with ERTS, Rep. PAPER-A20, NASA, Washington (DC), USA, 1973.
    Retrieved from: https://ntrs.nasa.gov/search.jsp?R=19740022614;
    Retrieved on: Mar. 12, 2018
  14. S. Lu et al., “A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons,” Plant Methods, vol. 14, no. 15, pp. 2 – 15, Feb. 2018.
    DOI: 10.1186/s13007-018-0281-z
    PMid: 29449875
    PMCid: PMC5812224
  15. C. Jurgens, “The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data,” Int. J. Remote Sens., vol. 18, no. 17, pp. 3583 – 3594, Nov. 1997.
    DOI: 10.1080/014311697216810
  16. C. B. Datt, “A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests Using Eucalyptus Leaves,” J. Plant Physiol., vol. 154, no. 1, pp. 30 – 36, Jan. 1999.
    DOI: 10.1016/S0176-1617(99)80314-9
  17. J. A. Gamon, J. Peñuelas, C. B. Field, “A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency,” Remote Sens. Environ., vol. 41, no. 1, pp. 35 – 44, Jul. 1992.
    DOI: 10.1016/0034-4257(92)90059-S
  18. M. F. Garbulsky, J. Peñuelas, J. A. Gamon, Y. Inoue, I. Filella, “The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis,” Remote Sens. Environ., vol. 115, no. 2, pp. 281 – 297, Feb. 2011.
    DOI: 10.1016/j.rse.2010.08.023
  19. G. A. Blackburn, “Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves,” Intern. Remote Sens., vol. 19, no. 4, pp. 657 – 675, 1998.
    DOI: 10.1080/014311698215919
  20. J. L. Rougean, F. M. Breon, “Estimating PAR absorbed by vegetation from bidirectional reflectance measurements,” Remote Sens. Environ., vol. 51, no. 3, pp. 375 – 384, Mar. 1995.
    DOI: 10.1016/0034-4257(94)00114-3
  21. A. A Gitelson, Y. Gritz, M. N. Merzlyak, “Relationships between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves,” J. Plant Physiol., vol. 160, no. 3, pp. 271 – 282, Mar. 2003.
    DOI: 10.1078/0176-1617-00887
  22. J. Vogelmann, B. Rock, D. Moss. “Red Edge Spectral Measurements from Sugar Maple Leaves,” Intern. J. Remote Sensing, vol. 14, no. 8, pp. 1563 – 1575, 1993.
    DOI: 10.1080/01431169308953986
  23. A. A. Gitelson, A. Viña, V. Ciganda, D. C. Rundquist, T. J. Arkebauer, “Remote estimation of canopy chlorophyll content in crops,” Geophys. Res. Lett., vol. 32, no. 8, L08403, Apr. 2005.
    DOI: 10.1029/2005GL022688
  24. M. S. Kim, “The use of narrow spectral bands for improving remote sensing estimation of fractionally absorbed photosynthetically active radiation (fAPAR),” M.Sc. dissertation, University of Maryland, Dept. of Geography, College Park (MD), USA, 1994.
  25. S. T. Daughtry, C. L. Walthall, M. S. Kim, E. B. de Colstoun, J. E. McMurtrey III, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ., vol. 74, no. 2, pp. 229 – 239, Nov. 2000.
    DOI: 10.1016/S0034-4257(00)00113-9
  26. N. Broge, E. Leblanc, “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area and canopy chlorophyll density,” Remote Sens. Environ., vol. 76, no. 2, pp. 156 – 172, May 2000.
  27. Ch. Zaiontz, Real Statistics Using Excel: Studentized Range q Table, Real Statistics.
    Retrieved from: http://www.real-statistics.com/statistics-tables/studentized-range-q-table/;
    Retrieved on: Mar. 12, 2018