Imaging Spectrometry in Agriculture - Plant Vitality And Yield Indicators

  • Jan G. P. W. Clevers
Part of the Eurocourses: Remote Sensing book series (EURS, volume 4)


For monitoring agricultural crop production, growth of crops has to be studied, e.g. by using crop growth models. Estimates of crop growth often are inaccurate for non-optimal growing conditions. Remote sensing can provide information on the actual status (e.g. its vitality) of agricultural crops. This information can be used to initialize, calibrate or update crop growth models, and it can yield parameter estimates to be used as direct input into growth models: (1) leaf area index (LAI), (2) leaf angle distribution (LAD) and (3) leaf colour (optical properties in the PAR region). LAI and LAD determine the amount of light interception. Leaf (or crop) colour influences the fraction of absorbed photosynthetically active radiation (APAR) and the maximum (potential) rate of photosynthesis of the leaves. A framework is described for integrating optical remote sensing data from various sources in order to estimate the mentioned parameters. Emphasis is on the importance of the red edge index as a measure for plant vitality. Imaging spectrometry data are needed for an accurate estimation of this red edge index.

The above concepts for crop growth estimation were elucidated and illustrated with a case study for sugar beet using groundbased and airborne data obtained during the MAC Europe 1991 campaign. A simple reflectance model was used for estimating LAI. Quantitative information concerning LAD was obtained by measurements at two viewing angles. The red edge index was used for estimating the leaf optical properties. Finally, a crop growth model (SUCROS) was calibrated on time-series of optical reflectance measurements to improve the estimation of beet yield.


Sugar Beet Leaf Area Index Imaging Spectroscopy Solar Zenith Angle Leaf Chlorophyll Content 
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.


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9. References

  1. Allen, W.A., H.W. Gausman, A.J. Richardson & J.R. Thomas (1969) ‘Interaction of isotropic light with a compact plant leaf, J. Opt. Soc. Am., 59, 1376–1379.CrossRefGoogle Scholar
  2. Allen, W.A., H.W. Gausman and A.J. Richardson (1970) ‘Mean effective optical constants of cotton leaves’, J. Opt. Soc. Am., 60, 542–547.Google Scholar
  3. Bonham-Carter, G.F. (1988) ‘Numerical procedures and computer program for fitting an inverted gaussian model to vegetation reflectance data’, Computers & Geosciences, vol. 14(3), 339–356.CrossRefGoogle Scholar
  4. Bouman, B.A.M. (1991) ‘Linking X-band radar backscattering and optical reflectance with crop growth models’, Thesis Agricultural University Wageningen, Wageningen, The Netherlands.Google Scholar
  5. Bouman, B.A.M. (1992a) ‘Accuracy of estimating the leaf area index from vegetation indices derived from reflectance characteristics, a simulation study’, International Journal of Remote Sensing, 13, 3069–3084.CrossRefGoogle Scholar
  6. Bouman, B.A.M. (1992b) ‘Linking physical remote sensing models with crop growth simulation models, applied for sugar beet’, International Journal of Remote Sensing, 13, 2565–2581.CrossRefGoogle Scholar
  7. Bouman, B.A.M. and J. Goudriaan (1989) ‘Estimation of crop growth from optical and microwave soil cover’, International Journal of Remote Sensing, 10, 1843–1855.CrossRefGoogle Scholar
  8. Bouman, B.A.M., H.W.J. van Kasteren and D. Uenk (1992) ‘Standard relations to estimate ground cover and LAI of agricultural crops from reflectance measurements’, Eur. J. Agronomy (in press).Google Scholar
  9. Bunnik, N.J.J. (1978) ‘The multispectral reflectance of shortwave radiation by agricultural crops in relation with their morphological and optical properties’, Ph.D. Thesis, Mededelingen Landbouwhogeschool Wageningen 78-1, 175 pp.Google Scholar
  10. Büker, C. and J.G.P.W. Clevers (1992) ‘Imaging spectroscopy for agricultural applications’, Report LUW-LMK-199206, Dept. Landsurveying & Remote Sensing, Wageningen Agricultural University.Google Scholar
  11. Büker, C., J.G.P.W. Clevers, H.J.C. van Leeuwen, B.A.M. Bouman, and D. Uenk (1992a) ‘Optical component MAC Europe, Ground truth report, Flevoland 1991’, Report LUW-LMK-199204, Dept. Landsurveying & Remote Sensing, Wageningen Agricultural University, 54 pp.Google Scholar
  12. Büker, C., J.G.P.W. Clevers, and H.J.C. van Leeuwen (1992b) ‘Optical component MAC Europe, Optical data report, Flevoland 1991’, Report LUW-LMK-199205, Dept. Landsurveying & Remote Sensing, Wageningen Agricultural University.Google Scholar
  13. Clevers, J.G.P.W. (1988) ‘The derivation of a simplified reflectance model for the estimation of leaf area index’, Remote Sensing of Environment, 25, 53–69.CrossRefGoogle Scholar
  14. Clevers, J.G.P.W. (1989) ‘The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture’, Remote Sensing of Environmen, 29, 25–37.CrossRefGoogle Scholar
  15. Clevers, J.G.P.W. (1992) ‘Modelling and synergistic use of optical and microwave remote sensing. Report 4: Influence of leaf properties on the relationship between WDVI and LAI: a sensitivity analysis with the SAIL and the PROSPECT model’, BCRS report 92-14, 36 pp.Google Scholar
  16. Clevers, J.G.P.W. and C. Büker (1991) ‘Feasibility of the red edge index for the detection of nitrogen deficiency’, Proc. 5th Int. Coll. on Physical Measurements and Signatures in Remote Sensing, Courchevel, France, ESA SP-319, 165–168.Google Scholar
  17. Clevers, J.G.P.W. and W. Verhoef (1990) ‘Modelling and synergistic use of optical and microwave remote sensing. Report 2: LAI estimation from canopy reflectance and WDVI: a sensitivity analysis with the SAIL model’, BCRS report 90-39, 70 pp.Google Scholar
  18. Clevers, J.G.P.W., W. Verhoef and H.J.C. van Leeuwen (1992) ‘Estimating APAR by means of vegetation indices: a sensitivity analysis’, International Archives of Photogrammetry and Remote Sensing, Vol. XXIX, Part B7, Comm. VII, XVIIth ISPRS Congress, Washington D.C., 1992, pp. 691–698.Google Scholar
  19. Curran, P.J. (1989) ‘Remote sensing of foliar chemistry’, Remote Sensing of Environment, 29, 271–278.CrossRefGoogle Scholar
  20. Goel, N.S. and D.W. Deering (1985) ‘Evaluation of a canopy reflectance model for LAI estimation through its inversion’, IEEE GE-23, 674–684.Google Scholar
  21. Goel, N.S. (1989) ‘Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data’, in G. Asrar (ed.) ‘Theory and applications of optical remote sensing’, J. Wiley & Sons, Inc., New York. 205–251.Google Scholar
  22. Goetz, A.F.H. (1991) ‘Imaging spectrometry for studying Earth, air, fire and water’, EARSeL Advances in Remote Sensing, 1, 3–15.Google Scholar
  23. Guyot, G. and F. Baret (1988) ‘Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux’, Proc. 4th Int. Coll. on Spectral Signatures of Objects in Remote Sensing, Aussois, France, 18-22 January 1988. ESA SP-287, 279–286.Google Scholar
  24. Horler, D.N.H., M. Dockray and J. Barber (1983) ‘The red edge of plant leaf reflectance’, International Journal of Remote Sensing, 4, 273–288.CrossRefGoogle Scholar
  25. Jacquemoud, S. (1992) ‘Utilisation de la haute resolution spectrale pour l’etude des couverts vegetaux: devellopement d’un modele de reflectance spectrale’, Ph.D. Thesis, University of Paris VII, 92 pp.Google Scholar
  26. Jacquemoud S. and F. Baret (1990) ‘PROSPECT: a model of leaf optical properties spectra’, Remote Sensing of Environment, 34, 75–91.CrossRefGoogle Scholar
  27. Kanemasu, E.T., G. Asrar and M. Fuchs (1984) ‘Application of remotely sensed data in wheat growth modelling’, in Day, W. and R.K. Atkin (ed.) ‘Wheat growth modelling’, Plenum Press, New York and London, Published in cooperation with NATO Scientific Affairs Division, 357–369.Google Scholar
  28. Looyen, W.J., W. Verhoef, J.G.P.W. Clevers, J.T. Lamers and J. Boerma (1991) ‘CAESAR: evaluation of the dual-look concept’, BCRS report 91-10, 144 pp.Google Scholar
  29. Maas, S.J. (1988) ‘Use of remotely sensed information in agricultural crop growth models’, Ecological modelling, 41, 247–268.CrossRefGoogle Scholar
  30. Penning de Vries, F.W.T. and H.H. van Laar (1982) ‘Simulation of plant growth and crop production’, Simulation Monographs, PUDOC, Wageningen, The Netherlands, 308 pp.Google Scholar
  31. Richardson, A.J. and C.L. Wiegand (1977) ‘Distinguishing vegetation from soil background information’, Photogr. Engineering and Remote Sensing, 43, 1541–1552.Google Scholar
  32. Spitters, C.J.T., H. van Keulen and D.W.G. van Kraalingen (1989) ‘A simple and universal crop growth simulator: SUCROS87’, in Rabbinge, R., S.A. Ward and H.H. van Laar (eds.) ’simulation and systems management in crop protection’, Simulation Monographs 32, PUDOC, Wageningen, The Netherlands, pp. 147–181.Google Scholar
  33. Steven M.D., P.V. Biscoe and K.W. Jaggard (1983) ‘Estimation of sugar beet productivity from reflection in the red and infrared spectral bands’, International Journal of Remote Sensing, 2, 117–125.Google Scholar
  34. Suits, G.H. (1972) ‘The calculation of the directional reflectance of a vegetation canopy’, Remote Sensing of Environment, 2, 117–125.CrossRefGoogle Scholar
  35. Tucker, C.J. (1979) ‘Red and photographic infrared linear combinations for monitoring vegetation’, Remote Sensing of Environment, 8, 127–150.CrossRefGoogle Scholar
  36. Uenk, D., B.A.M. Bouman and H.W.J. van Kasteren (1992) ‘Reflectiemetingen aan landbouwgewassen, Handleiding voor het meten van gewasreflectie’, Standaardlijnen voor de bepaling van bodembedekking en LAI (in Dutch). CABO-DLO report 156, 56 pp.Google Scholar
  37. Vane, G., M. Chrisp, H. Enmark, S. Macenka and J. Solomon (1984) ‘Airborne Visible/Infrared Imaging Spectrometer: An advanced tool for Earth remote sensing’, Proc. IGARSS’ 84, SP215, 751.Google Scholar
  38. Vane, G. and A.F.H. Goetz (1988) ‘Terrestrial imaging spectroscopy’, Remote Sensing of Environment, 24, 1–29.CrossRefGoogle Scholar
  39. Verhoef, W. (1984) ‘Light scattering by leaf layers with application to canopy reflectance modelling: the SAIL model’, Remote Sensing of Environment, 16, 125–141.CrossRefGoogle Scholar
  40. Verhoef, W. and N.J.J. Bunnik (1981) ‘Influence of crop geometry on multispectral reflectance determined by the use of canopy reflectance models’, Proc. Int. Coll. on Signatures of Remotely Sensed Objects, Avignon, France, 273–290.Google Scholar
  41. Wit, C.T. de (1965) ‘Photosynthesis of leaf canopies’, Agricultural Research Report 663, PUDOC, Wageningen, The Netherlands.Google Scholar

Copyright information

© ECSC, EEC, EAEC, Brussels and Luxembourg 1994

Authors and Affiliations

  • Jan G. P. W. Clevers
    • 1
  1. 1.Dept. Landsurveying and Remote SensingWageningen Agricultural UniversityAH WageningenThe Netherlands

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