Beyond NDVI: Extraction of Biophysical Variables From Remote Sensing Imagery

  • J. G. P. W. Clevers
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 18)


This chapter provides an overview of methods used for the extraction of biophysical vegetation variables from remote sensing imagery. It starts with the description of the main spectral regions in the optical window of the electromagnetic spectrum based on typical spectral signatures of land surfaces. Subsequently, the merit and problems of using radiative transfer models to describe the relationship between spectral measurements and biophysical and chemical variables of vegetation are described. Next, the use of statistical methods by means of vegetation indices for the same purpose gets attention. An overview of different types of indices is given without having the ambition in being exhaustive. Subsequently, an overview is provided of the biogeophysical vegetation variables that can directly be estimated from optical remote sensing observations, with emphasis on using vegetation indices. These vegetation variables are: (1) chlorophyll and nitrogen, (2) vegetation cover fraction and fAPAR, (3) leaf area index, and (4) canopy water. Finally, an outlook for a major research direction in the near future in this context is provided.


Normalize Difference Vegetation Index Vegetation Index Leaf Area Index Radiative Transfer Model Bidirectional Reflectance Distribution Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Asrar G, Myneni RB, Choudhury BJ (1992) Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: a modeling study. Remote Sens Environ 41:85–103CrossRefGoogle Scholar
  2. Bacour C, Jacquemoud S, Tourbier Y, Dechambre M, Frangi JP (2002) Design and analysis of numerical experiments to compare four canopy reflectance models. Remote Sens Environ 79:72–83CrossRefGoogle Scholar
  3. Bacour C, Baret F, Béal D, Weiss M, Pavageau K (2006) Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: principles and validation. Remote Sens Environ 105:313–325CrossRefGoogle Scholar
  4. Baret F, Buis S (2008) Estimating canopy characteristics from remote sensing observations: review of methods and associated problems. In: Liang S (ed) Advances in land remote sensing: system, modeling, inversion and application, pp 173–201Google Scholar
  5. Baret F, Guyot G, Major DJ (1989) TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In: Digest – international geoscience and remote sensing symposium (IGARSS), Vancouver, 10–14 July 1989, pp 1355–1358Google Scholar
  6. Baret F, Houlès V, Guérif M (2007) Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. J Exp Bot 58:869–880CrossRefGoogle Scholar
  7. Barnsley MJ, Strahler AH, Morris KP, Muller JP (1994) Sampling the surface bidirectional reflectance distribution function (BRDF): 1. evaluation of current and future satellite sensors. Remote Sens Rev 8:271–311CrossRefGoogle Scholar
  8. Bouman BAM, Van Kasteren HWJ, Uenk D (1992) Standard relations to estimate ground cover and LAI of agricultural crops from reflectance measurements. ISPRS J Photogramm Remote Sens 4:249–262Google Scholar
  9. Broge NH, 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 Sens Environ 76:156–172CrossRefGoogle Scholar
  10. Broge NH, Mortensen JV (2002) Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sens Environ 81:45–57CrossRefGoogle Scholar
  11. Clevers JGPW (1988) The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sens Environ 25:53–69CrossRefGoogle Scholar
  12. Clevers JGPW (1989) Application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sens Environ 29:25–37CrossRefGoogle Scholar
  13. Clevers JGPW (1999) The use of imaging spectrometry for agricultural applications. ISPRS J Photogramm Remote Sens 54:299–304CrossRefGoogle Scholar
  14. Clevers JGPW, Kooistra L (2012) Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content. IEEE J Sel Top Appl Earth Obs Remote Sens 5:574–583CrossRefGoogle Scholar
  15. Clevers JGPW, Van Leeuwen HJC, Verhoef W (1994) Estimating the fraction APAR by means of vegetation indices: a sensitivity analysis with a combined PROSPECT-SAIL model. Remote Sens Rev 9:203–220CrossRefGoogle Scholar
  16. Clevers JGPW, Verhoef W (1993) LAI estimation by means of the WDVI: a sensitivity analysis with a combined PROSPECT-SAIL model. Remote Sens Rev 7:43–64CrossRefGoogle Scholar
  17. Clevers JGPW, de Jong SM, Epema GF, van der Meer F, Bakker WH, Skidmore AK, Addink EA (2001) MERIS and the red-edge position. Int J Appl Earth Obs Geoinf 3:313–320CrossRefGoogle Scholar
  18. Clevers JGPW, De Jong SM, Epema GF, Van der Meer FD, Bakker WH, Skidmore AK, Scholte KH (2002) Derivation of the red edge index using the MERIS standard band setting. Int J Remote Sens 23:3169–3184CrossRefGoogle Scholar
  19. Clevers JGPW, Kooistra L, Salas EAL (2004) Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data. Int J Remote Sens 25:3883–3895CrossRefGoogle Scholar
  20. Clevers JGPW, Kooistra L, Schaepman ME (2008) Using spectral information from the NIR water absorption features for the retrieval of canopy water content. Int J Appl Earth Obs Geoinf 10:388–397CrossRefGoogle Scholar
  21. Clevers JGPW, Kooistra L, Schaepman ME (2010) Estimating canopy water content using hyperspectral remote sensing data. Int J Appl Earth Obs Geoinf 12:119–125CrossRefGoogle Scholar
  22. Coburn CA, Van Gaalen E, Peddle DR, Flanagan LB (2010) Anisotropic reflectance effects on spectral indices for estimating ecophysiological parameters using a portable goniometer system. Can J Remote Sens 36:S355–S364CrossRefGoogle Scholar
  23. Collins W (1978) Remote sensing of crop type and maturity. Photogramm Eng Remote Sens 44:42–55Google Scholar
  24. Combal B, Baret F, Weiss M, Trubuil A, Mace D, Pragnere A, Myneni R, Knyazikhin Y, Wang L (2003) Retrieval of canopy biophysical variables from bidirectional reflectance – using prior information to solve the ill-posed inverse problem. Remote Sens Environ 84:1–15CrossRefGoogle Scholar
  25. Crist EP, Cicone RC (1984) Application of the Tasseled Cap concept to simulated Thematic Mapper data. Photogramm Eng Remote Sens 50:343–352Google Scholar
  26. Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278CrossRefGoogle Scholar
  27. Curran PJ, Dungan JL, Peterson DL (2001) Estimating the foliar biochemical concentration of leaves with reflectance spectrometry testing the Kokaly and Clark methodologies. Remote Sens Environ 76:349–359CrossRefGoogle Scholar
  28. Danson FM, Steven MD, Malthus TJ, Clark JA (1992) High-spectral resolution data for determining leaf water content. Int J Remote Sens 13:461–470CrossRefGoogle Scholar
  29. Dash J, Curran PJ (2004) The MERIS terrestrial chlorophyll index. Int J Remote Sens 25:5403–5413CrossRefGoogle Scholar
  30. Daughtry CST, Gallo KP, Goward SN, Prince SD, Kustas WP (1992) Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies. Remote Sens Environ 39:141–152CrossRefGoogle Scholar
  31. Daughtry CST, Walthall CL, Kim MS, Brown de Colstoun E, McMurtrey JE III (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74:229–239CrossRefGoogle Scholar
  32. Delegido J, Verrelst J, Alonso L, Moreno J (2011) Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 11:7063–7081CrossRefGoogle Scholar
  33. ESA (2006) The changing Earth. In: Battrick B (ed) ESA Publication. ESA, Noordwijk, p 83Google Scholar
  34. Gamon JA, Field CB, Bilger W, Björkman O, Fredeen AL, Peñuelas J (1990) Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85:1–7CrossRefGoogle Scholar
  35. Gamon JA, Serrano L, Surfus JS (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112:492–501CrossRefGoogle Scholar
  36. Gao BC (1996) NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266CrossRefGoogle Scholar
  37. Gao BC, Goetz AFH (1990) Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J Geophys Res 95:3549–3564CrossRefGoogle Scholar
  38. Garbulsky MF, Peñuelas J, Gamon J, Inoue Y, Filella I (2011) 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 115:281–297CrossRefGoogle Scholar
  39. Garrigues S, Lacaze R, Baret F, Morisette JT, Weiss M, Nickeson JE, Fernandes R, Plummer S, Shabanov NV, Myneni RB, Knyazikhin Y, Yang W (2008) Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. J Geophys Res G Biogeosci 113, art no. G02028Google Scholar
  40. Gitelson AA, Merzlyak MN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148:494–500CrossRefGoogle Scholar
  41. Gitelson AA, Gritz Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol 160:271–282CrossRefGoogle Scholar
  42. Gitelson AA, Keydan GP, Merzlyak MN (2006a) Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys Res Lett 33, art. no. L11402Google Scholar
  43. Gitelson AA, Viña A, Verma SB, Rundquist DC, Arkebauer TJ, Keydan G, Leavitt B, Ciganda V, Burba GG, Suyker AE (2006b) Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J Geophys Res D Atmos 111, art. no. D08S11Google Scholar
  44. Gong P, Pu RL, Biging GS, Larrieu MR (2003) Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Trans Geosci Remote Sens 41:1355–1362CrossRefGoogle Scholar
  45. Goward SN, Huemmrich KF (1992) Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model. Remote Sens Environ 39:119–140CrossRefGoogle Scholar
  46. Guyot G, Baret F (1988) Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux. In: Proceedings of the 4th international colloquium ‘spectral signatures of objects in remote sensing’, Aussois, France: ESA, Paris, pp 279–286Google Scholar
  47. Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ 81:416–426CrossRefGoogle Scholar
  48. Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90:337–352CrossRefGoogle Scholar
  49. Hardisky MA, Klemas V, Smart RM (1983) The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm Eng Remote Sens 49:77–83Google Scholar
  50. Horler DNH, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4:273–288CrossRefGoogle Scholar
  51. Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309CrossRefGoogle Scholar
  52. Iqbal M (1983) An introduction to solar radiation. Academic, OntarioGoogle Scholar
  53. Jacquemoud S, Bacour C, Poilve H, Frangi JP (2000) Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sens Environ 74:471–481CrossRefGoogle Scholar
  54. Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner GP, François C, Ustin SL (2009) PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sens Environ 113:S56–S66CrossRefGoogle Scholar
  55. Jongschaap REE, Booij R (2004) Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy nitrogen status. Int J Appl Earth Obs Geoinf 5:205–218CrossRefGoogle Scholar
  56. Kauth RJ, Thomas GS (1976) The tasseled cap – a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In: Proceedings of the symposium on machine processing of remotely sensed data, 4B, Purdue University, West Lafayette, pp 41–51Google Scholar
  57. Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159CrossRefGoogle Scholar
  58. Kokaly RF, Clark RN (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ 67:267–287CrossRefGoogle Scholar
  59. Kokaly RF, Despain DG, Clark RN, Livo KE (2003) Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sens Environ 84:437–456CrossRefGoogle Scholar
  60. Laurent VCE, Verhoef W, Clevers JGPW, Schaepman ME (2011) Inversion of a coupled canopy-atmosphere model using multi-angular top-of-atmosphere radiance data: a forest case study. Remote Sens Environ 115:2603–2612CrossRefGoogle Scholar
  61. Liang S (2004) Quantitative remote sensing of land surfaces. Wiley, HobokenGoogle Scholar
  62. Mitscherlich A (1920) Das Liebigsche Gesetz vom Minimum und das Wirkungsgesetz der Wachstumsfaktoren. Naturwissenschaften 8:85–88CrossRefGoogle Scholar
  63. Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995a) Interpretation of spectral vegetation indexes. IEEE Trans Geosci Remote Sens 33:481–486CrossRefGoogle Scholar
  64. Myneni RB, Maggion S, Iaquinta J, Privette JL, Gobron N, Pinty B, Kimes DS, Verstraete MM, Williams DL (1995b) Optical remote sensing of vegetation: modeling, caveats, and algorithms. Remote Sens Environ 51:169–188CrossRefGoogle Scholar
  65. Peñuelas J, Filella I, Biel C, Serrano L, Save R (1993) The reflectance at the 950–970 nm region as an indicator of plant water status. Int J Remote Sens 14:1887–1905CrossRefGoogle Scholar
  66. Peñuelas J, Filella I, Serrano L, Save R (1996) Cell wall elasticity and water index (R970 nm/R900 nm) in wheat under different nitrogen availabilities. Int J Remote Sens 17:373–382CrossRefGoogle Scholar
  67. Pinty B, Verstraete MM (1992) On the design and validation of surface bidirectional reflectance and albedo models. Remote Sens Environ 41:155–167CrossRefGoogle Scholar
  68. Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43:1541–1552Google Scholar
  69. Rollin EM, Milton EJ (1998) Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sens Environ 65:86–92CrossRefGoogle Scholar
  70. Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107CrossRefGoogle Scholar
  71. Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: Earth resources technology satellite-1 symposium, Goddard Space Flight Center, Washington, DC, pp 309–317Google Scholar
  72. Rouse JW, Haas RH, Deering DW, Schell JA, Harlan JC (1974) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. In: NASA/GSFC type III final report, Greenbelt, MD, p 371Google Scholar
  73. Schlerf M, Atzberger C, Hill J (2005) Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens Environ 95:177–194CrossRefGoogle Scholar
  74. Sims DA, Gamon JA (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sens Environ 84:526–537CrossRefGoogle Scholar
  75. Stimson HC, Breshears DD, Ustin SL, Kefauver SC (2005) Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens Environ 96:108–118CrossRefGoogle Scholar
  76. Thenkabail PS, Smith RB, De Pauw E (2002) Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogramm Eng Remote Sens 68:607–621Google Scholar
  77. Tucker CJ (1980) Remote sensing of leaf water content in the near infrared. Remote Sens Environ 10:23–32CrossRefGoogle Scholar
  78. Verger A, Baret F, Weiss M (2011) A multisensor fusion approach to improve LAI time series. Remote Sens Environ 115:2460–2470CrossRefGoogle Scholar
  79. Verrelst J, Schaepman ME, Koetz B, Kneubühler M (2008) Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sens Environ 112:2341–2353CrossRefGoogle Scholar
  80. WMO/IOC (2010) Implementation plan for the global observing system for climate in support of the UNFCCC (2010 Update). Report GCOS-138/GOOS-184/GTOS-76/WMO-TD/No. 1523, p 180Google Scholar
  81. Wu C, Niu Z, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agr Forest Meteorol 148:1230–1241CrossRefGoogle Scholar
  82. Yoder BJ, Pettigrew-Crosby RE (1995) Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sens Environ 53:199–211CrossRefGoogle Scholar
  83. Zarco-Tejada PJ, Berjon A, Lopez-Lozano R, Miller JR, Martin P, Cachorro V, Gonzalez MR, de Frutos A (2005) Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens Environ 99:271–287CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Centre for Geo-InformationWageningen UniversityWageningenNetherlands

Personalised recommendations