Skip to main content
SpringerLink
Log in

Beyond NDVI: Extraction of Biophysical Variables From Remote Sensing Imagery

  • Chapter
  • First Online:
Book cover Land Use and Land Cover Mapping in Europe

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 18))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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 41:85–103

    Article  Google Scholar 

  • 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 79:72–83

    Article  Google Scholar 

  • 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 105:313–325

    Article  Google Scholar 

  • 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 173–201

    Google Scholar 

  • 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 1355–1358

    Google Scholar 

  • 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 58:869–880

    Article  Google Scholar 

  • 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 8:271–311

    Article  Google Scholar 

  • 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 4:249–262

    Google Scholar 

  • 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 76:156–172

    Article  Google Scholar 

  • Broge NH, Mortensen JV (2002) Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sens Environ 81:45–57

    Article  Google Scholar 

  • Clevers JGPW (1988) The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sens Environ 25:53–69

    Article  Google Scholar 

  • Clevers JGPW (1989) Application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sens Environ 29:25–37

    Article  Google Scholar 

  • Clevers JGPW (1999) The use of imaging spectrometry for agricultural applications. ISPRS J Photogramm Remote Sens 54:299–304

    Article  Google Scholar 

  • 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 5:574–583

    Article  Google Scholar 

  • 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 9:203–220

    Article  Google Scholar 

  • Clevers JGPW, Verhoef W (1993) LAI estimation by means of the WDVI: a sensitivity analysis with a combined PROSPECT-SAIL model. Remote Sens Rev 7:43–64

    Article  Google Scholar 

  • 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 3:313–320

    Article  Google Scholar 

  • 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 23:3169–3184

    Article  Google Scholar 

  • 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 25:3883–3895

    Article  Google Scholar 

  • 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 10:388–397

    Article  Google Scholar 

  • Clevers JGPW, Kooistra L, Schaepman ME (2010) Estimating canopy water content using hyperspectral remote sensing data. Int J Appl Earth Obs Geoinf 12:119–125

    Article  Google Scholar 

  • 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–S364

    Article  Google Scholar 

  • Collins W (1978) Remote sensing of crop type and maturity. Photogramm Eng Remote Sens 44:42–55

    Google Scholar 

  • 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 84:1–15

    Article  Google Scholar 

  • Crist EP, Cicone RC (1984) Application of the Tasseled Cap concept to simulated Thematic Mapper data. Photogramm Eng Remote Sens 50:343–352

    Google Scholar 

  • Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278

    Article  Google Scholar 

  • 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 76:349–359

    Article  Google Scholar 

  • Danson FM, Steven MD, Malthus TJ, Clark JA (1992) High-spectral resolution data for determining leaf water content. Int J Remote Sens 13:461–470

    Article  Google Scholar 

  • Dash J, Curran PJ (2004) The MERIS terrestrial chlorophyll index. Int J Remote Sens 25:5403–5413

    Article  Google Scholar 

  • 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 39:141–152

    Article  Google Scholar 

  • 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 74:229–239

    Article  Google Scholar 

  • 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 11:7063–7081

    Article  Google Scholar 

  • ESA (2006) The changing Earth. In: Battrick B (ed) ESA Publication. ESA, Noordwijk, p 83

    Google Scholar 

  • 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 85:1–7

    Article  Google Scholar 

  • 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 112:492–501

    Article  Google Scholar 

  • Gao BC (1996) NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266

    Article  Google Scholar 

  • Gao BC, Goetz AFH (1990) Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J Geophys Res 95:3549–3564

    Article  Google Scholar 

  • 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 115:281–297

    Article  Google Scholar 

  • 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. G02028

    Google Scholar 

  • Gitelson AA, Merzlyak MN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148:494–500

    Article  Google Scholar 

  • 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 160:271–282

    Article  Google Scholar 

  • 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. L11402

    Google Scholar 

  • 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. D08S11

    Google Scholar 

  • 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 41:1355–1362

    Article  Google Scholar 

  • Goward SN, Huemmrich KF (1992) Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model. Remote Sens Environ 39:119–140

    Article  Google Scholar 

  • 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 279–286

    Google Scholar 

  • 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 81:416–426

    Article  Google Scholar 

  • 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 90:337–352

    Article  Google Scholar 

  • 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 49:77–83

    Google Scholar 

  • Horler DNH, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4:273–288

    Article  Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309

    Article  Google Scholar 

  • Iqbal M (1983) An introduction to solar radiation. Academic, Ontario

    Google Scholar 

  • 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 74:471–481

    Article  Google Scholar 

  • 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–S66

    Article  Google Scholar 

  • 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 5:205–218

    Article  Google Scholar 

  • 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 41–51

    Google Scholar 

  • Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159

    Article  Google Scholar 

  • 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 67:267–287

    Article  Google Scholar 

  • 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 84:437–456

    Article  Google Scholar 

  • 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 115:2603–2612

    Article  Google Scholar 

  • Liang S (2004) Quantitative remote sensing of land surfaces. Wiley, Hoboken

    Google Scholar 

  • Mitscherlich A (1920) Das Liebigsche Gesetz vom Minimum und das Wirkungsgesetz der Wachstumsfaktoren. Naturwissenschaften 8:85–88

    Article  Google Scholar 

  • Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995a) Interpretation of spectral vegetation indexes. IEEE Trans Geosci Remote Sens 33:481–486

    Article  Google Scholar 

  • 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 51:169–188

    Article  Google Scholar 

  • 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 14:1887–1905

    Article  Google Scholar 

  • 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 17:373–382

    Article  Google Scholar 

  • Pinty B, Verstraete MM (1992) On the design and validation of surface bidirectional reflectance and albedo models. Remote Sens Environ 41:155–167

    Article  Google Scholar 

  • Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43:1541–1552

    Google Scholar 

  • Rollin EM, Milton EJ (1998) Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sens Environ 65:86–92

    Article  Google Scholar 

  • Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107

    Article  Google Scholar 

  • 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 309–317

    Google Scholar 

  • 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 371

    Google Scholar 

  • Schlerf M, Atzberger C, Hill J (2005) Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens Environ 95:177–194

    Article  Google Scholar 

  • 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 84:526–537

    Article  Google Scholar 

  • 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 96:108–118

    Article  Google Scholar 

  • 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 68:607–621

    Google Scholar 

  • Tucker CJ (1980) Remote sensing of leaf water content in the near infrared. Remote Sens Environ 10:23–32

    Article  Google Scholar 

  • Verger A, Baret F, Weiss M (2011) A multisensor fusion approach to improve LAI time series. Remote Sens Environ 115:2460–2470

    Article  Google Scholar 

  • Verrelst J, Schaepman ME, Koetz B, Kneubühler M (2008) Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sens Environ 112:2341–2353

    Article  Google Scholar 

  • 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 180

    Google Scholar 

  • Wu C, Niu Z, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agr Forest Meteorol 148:1230–1241

    Article  Google Scholar 

  • 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 53:199–211

    Article  Google Scholar 

  • 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 99:271–287

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Geo-Information, Wageningen University, 47, 6700 AA, Wageningen, Netherlands

    J. G. P. W. Clevers

Authors
  1. J. G. P. W. Clevers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. G. P. W. Clevers .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Center for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Manakos

  2. Department of Geography, University of Erlangen-Nürnberg, Erlangen, Germany

    Matthias Braun

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Clevers, J.G.P.W. (2014). Beyond NDVI: Extraction of Biophysical Variables From Remote Sensing Imagery. In: Manakos, I., Braun, M. (eds) Land Use and Land Cover Mapping in Europe. Remote Sensing and Digital Image Processing, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7969-3_22

Download citation

Publish with us

Policies and ethics

Search

Navigation