Precision Agriculture

, Volume 9, Issue 5, pp 303–319 | Cite as

A broad-band leaf chlorophyll vegetation index at the canopy scale

  • M. VinciniEmail author
  • E. Frazzi
  • P. D’Alessio


An assessment of the sensitivity at the canopy scale to leaf chlorophyll concentration of the broad-band chlorophyll vegetation index (CVI) is carried out for a wide range of soils and crops conditions and for different sun zenith angles by the analysis of a large synthetic dataset obtained by using in the direct mode the coupled PROSPECT + SAILH leaf and canopy reflectance model. An optimized version (OCVI) of the CVI is proposed. A single correction factor is incorporated in the OCVI algorithm to take into account the different spectral behaviors due to crop and soil types, sensor spectral resolution and scene sun zenith angle. An estimate of the value of the correction factor and of the minimum leaf area index (LAI) value of applicability are given for each considered condition. The results of the analysis of the synthetic dataset indicated that the broad-band CVI index could be used as a leaf chlorophyll estimator for planophile crops in most soil conditions. Results indicated as well that, in principle, a single correction factor incorporated in the OCVI could take into account the different spectral behaviors due to crop and soil types, sensor spectral resolution and scene sun zenith angle.


Remote sensing Vegetation indices Leaf chlorophyll concentration Variable rate fertilization 



The work was partly funded by the Regione Emilia Romagna in the context of the CITIMAP (Centre for the application of Remote Sensing in Precision Agriculture Mechanization) precision farming research program. Thanks are also expressed to Frédéric Baret of INRA for providing the models code.


  1. Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing Environment, 35(2–3), 161–173. doi: 10.1016/0034-4257(91)90009-U.CrossRefGoogle Scholar
  2. Baret, F., Jacquemoud, S., Guyot, G., & Leprieur, C. (1992). Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sensing Environment, 41(2–3), 133–142. doi: 10.1016/0034-4257(92)90073-S.CrossRefGoogle Scholar
  3. Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing Environment, 66(3), 273–285. doi: 10.1016/S0034-4257(98)00059-5.CrossRefGoogle Scholar
  4. Blackmer, T. M., Shepers, J. S., & Varvel, G. V. (1994). Light reflectance compared with other nitrogen stress measurements in corn leaves. Agronomy Journal, 86(6), 934–938.Google Scholar
  5. Baugh, W. M., & Groeneveld, D. P. (2006). Broadband vegetation index performance evaluated for a low-cover environment. International Journal of Remote Sensing 27(21–22),4715–4730.Google Scholar
  6. Broge, N. H., & Leblanc, E. (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environment, 76(2), 156–172. doi: 10.1016/S0034-4257(00)00197-8.CrossRefGoogle Scholar
  7. Broge, N. H., & Mortensen, J. V. (2002). Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing Environment, 81(1), 45–57. doi: 10.1016/S0034-4257(01)00332-7.CrossRefGoogle Scholar
  8. Daughtry, C. S. T., McMurtrey, J. E, III., Kim, M. S., & Chappelle, E. W. (1997). Estimating crop residue cover by blue fluorescence imaging. Remote Sensing Environment, 60(1), 14–21. doi: 10.1016/S0034-4257(96)00118-6.CrossRefGoogle Scholar
  9. Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E, III. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing Environment, 74(2), 229–239. doi: 10.1016/S0034-4257(00)00113-9.CrossRefGoogle Scholar
  10. Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and ear-infrared vegetation indices. Remote Sensing Environment, 54(1), 38–48. doi: 10.1016/0034-4257(95)00132-K.CrossRefGoogle Scholar
  11. Gamon, J. A., & Surfus, J. S. (1999). Assessing leaf pigment content and activity with a reflectometer. The New Phytologist, 143(1), 105–117. doi: 10.1046/j.1469-8137.1999.00424.x.CrossRefGoogle Scholar
  12. Gitelson, A. A., Kaufman, J. Y., & Merzlyac, M. N. (1996). Use of a Green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing Environment, 58(3), 289–298. doi: 10.1016/S0034-4257(96)00072-7.CrossRefGoogle Scholar
  13. Gitelson, A. A., & Merzlyac, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiolology, 148(1), 494–500.Google Scholar
  14. Haboudane, D., Miller, J., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integration of narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing Environment, 81(2–3), 416–426. doi: 10.1016/S0034-4257(02)00018-4.CrossRefGoogle Scholar
  15. Horler, D. N. H., Dockray, M., & Barber, J. (1983). The red-edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2), 273–288. doi: 10.1080/01431168308948546.CrossRefGoogle Scholar
  16. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing Environment, 25(3), 295–309. doi: 10.1016/0034-4257(88)90106-X.CrossRefGoogle Scholar
  17. Jacquemoud, S. (1993). Inversion of the PROSPECT + SAIL canopy reflectance model from AVIRIS equivalent spectra: Theoretical study. Remote Sensing Environment, 44(2–3), 281–292. doi: 10.1016/0034-4257(93)90022-P.CrossRefGoogle Scholar
  18. Jacquemoud, S., Bacour, C., Poilve, H., & Frangi, J. P. (2000). Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sensing Environment, 74(3), 417–481. doi: 10.1016/S0034-4257(00)00139-5.CrossRefGoogle Scholar
  19. Jacquemoud, S., Baret, F., Andrieu, B., Danson, M., & Jaggard, K. (1995). Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data Application to TM and AVIRIS sensors. Remote Sensing Environment, 52(3), 163–172. doi: 10.1016/0034-4257(95)00018-V.CrossRefGoogle Scholar
  20. Myneni, R. B., Hall, F. G., Sellers, P. J., & Markshalk, A. L. (1995). The interpretation of spectral vegetation indices. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 481–486. doi: 10.1109/36.377948.CrossRefGoogle Scholar
  21. Schepers, J. S., Blackmer, T. M., Wilhelm, W. W., & Resende, M. (1996). Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply. Journal of Plant Physiology, 148, 523–529.Google Scholar
  22. Soil Survey Staff. (1975). Soil taxonomy. A basic system of soil classification for making and interpreting soil surveys. Agriculture handbook Number 436, USDA & NRC Service. U.S. Government Printing Office, Wahington, D.C., USA, p. 754.Google Scholar
  23. Vincini, M., Frazzi, E., & D’Alessio, P. (2007). Comparison of narrow-band and broad-band vegetation indexes for canopy chlorophyll density estimation in sugar beet. In J. V. Stafford (Ed.), Precision agriculture ‘07: Proceedings of the 6th European Conference on Precision Agriculture (pp. 189–196). Wageningen, The Netherlands:Wageningen Academic Publishers. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Università Cattolica del Sacro Cuore-CRASTPiacenzaItaly

Personalised recommendations