Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density

Comparing prediction power and stability of broadband and hyperspectral vegetation indices for... Hyperspectral reflectance data representing a wide range of canopies were simulated using the combined PROSPECT+SAIL model. The simulations were used to study the stability of recently proposed vegetation indices (VIs) derived from adjacent narrowband spectral reflectance data across the visible (VIS) and near infrared (NIR) region of the electromagnetic spectrum. The prediction power of these indices with respect to green leaf area index (LAI) and canopy chlorophyll density (CCD) was compared, and their sensitivity to canopy architecture, illumination geometry, soil background reflectance, and atmospheric conditions were analyzed. The second soil-adjusted vegetation index (SAVI2) proved to be the best overall choice as a greenness measure. However, it is also shown that the dynamics of the VIs are very different in terms of their sensitivity to the different external factors that affects the spectral reflectance signatures of the various modeled canopies. It is concluded that hyperspectral indices are not necessarily better at predicting LAI and CCD, but that selection of a VI should depend upon (1) which parameter that needs to be estimated (LAI or CCD), (2) the expected range of this parameter, and (3) a priori knowledge of the variation of external parameters affecting the spectral reflectance of the canopy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density

Remote Sensing of Environment, Volume 76 (2) – May 1, 2001

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Publisher
Elsevier
Copyright
Copyright © 2001 Elsevier Science Inc.
ISSN
0034-4257
DOI
10.1016/S0034-4257(00)00197-8
Publisher site
See Article on Publisher Site

Abstract

Hyperspectral reflectance data representing a wide range of canopies were simulated using the combined PROSPECT+SAIL model. The simulations were used to study the stability of recently proposed vegetation indices (VIs) derived from adjacent narrowband spectral reflectance data across the visible (VIS) and near infrared (NIR) region of the electromagnetic spectrum. The prediction power of these indices with respect to green leaf area index (LAI) and canopy chlorophyll density (CCD) was compared, and their sensitivity to canopy architecture, illumination geometry, soil background reflectance, and atmospheric conditions were analyzed. The second soil-adjusted vegetation index (SAVI2) proved to be the best overall choice as a greenness measure. However, it is also shown that the dynamics of the VIs are very different in terms of their sensitivity to the different external factors that affects the spectral reflectance signatures of the various modeled canopies. It is concluded that hyperspectral indices are not necessarily better at predicting LAI and CCD, but that selection of a VI should depend upon (1) which parameter that needs to be estimated (LAI or CCD), (2) the expected range of this parameter, and (3) a priori knowledge of the variation of external parameters affecting the spectral reflectance of the canopy.

Journal

Remote Sensing of EnvironmentElsevier

Published: May 1, 2001

References

  • Estimating crop residue cover by blue fluorescence imaging
    Daughtry, C.S.T.; McMurtrey, J.E.; Kim, M.S.; Chappelle, E.W.
  • Comparison of broad-band and narrow-band red and near-infrared vegetation indices
    Elvidge, C.D.; Chen, Z.
  • Analyses of spectral–biophysical relationships for a corn canopy
    Gilabert, M.A.; Gandia, S.; Melia, J.
  • Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors
    Jacquemoud, S.; Baret, F.; Andrieu, B.; Danson, F.M.; Jaggard, K.
  • Estimating leaf biochemistry using the PROSPECT leaf optical properties model
    Jacquemoud, S.; Ustin, S.L.; Verdebout, J.; Schmuck, G.; Andreoli, G.; Hosgood, B.
  • A modified soil adjusted vegetation index
    Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S.
  • Estimating PAR absorbed by vegetation from bidirectional reflectance measurements
    Roujean, J.L.; Breon, F.M.

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