Scientific Issues and Instrumental Opportunities in Remote Sensing and High Resolution Spectrometry

  • Michel M. Verstraete
Part of the Eurocourses: Remote Sensing book series (EURS, volume 4)


The effective use of remote sensing techniques requires a basic understanding of the fundamental processes that affect radiation during its transport between the source of light, the target of interest, and the detector. The principles of radiation emission and scattering in the optical domain are outlined, paying particular attention to the spatial, temporal, spectral, and directional sources of variability in the data. The problems of measuring and interpreting these observations are addressed, and the specifications of existing and planned space-borne instruments are discussed.


Remote Sensing Spectral Band Coastal Ocean Advanced Very High Resolution Radiometer Advanced Very High Resolution Radiometer 
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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8. References

  1. Butler, D. (editor) (1987) ‘From pattern to process: The strategy of the Earth Observing System’, Report of the Eos Science Steering Committee, Vol II.Google Scholar
  2. Curran, P. J. (1981) ‘Multispectral remote sensing for estimating biomass and productivity’, in: Smith (ed.) Plants and the daylight spectrum, Academic Press, London, 65–96.Google Scholar
  3. ESA (1992) ‘The Medium Resolution Imaging Spectrometer (MERIS)’, Draft internal document, European Space Agency, 92 pp.Google Scholar
  4. Estes, J. and M. Consentino (1989) ‘Remote sensing of vegetation’, in M. Rambler, L. Margulis, and R. Fester (eds.) Global ecology, Academic Press, New York, 75–111.Google Scholar
  5. Freden, S. and F. Gordon, Jr. (1983) ‘Landsat satellites’, in R. Colwell (ed.) ‘Manual of Remote Sensing, Vol. 1’, American Society of Photogrammetry, Falls Church, 517–570.Google Scholar
  6. Houghton, J. (1986) ‘The Physics of Atmospheres’, Cambridge University Press, Cambridge, 271 pp.Google Scholar
  7. NASA (1986) ‘Data and information system: Report of the EOS Data Panel’, in Earth Observing System, Vol IIa, NASA Technical Memorandum 87777, Washington, 49 pp.Google Scholar
  8. Kaufman, Y. and D. Tanré (1992) ‘Atmospherically resistant vegetation index (ARVI) for EOSMODIS’, IEEE Transactions on Geoscience and Remote Sensing 30, 261–270.CrossRefGoogle Scholar
  9. Kidwell, K. (1991) ‘TCOAA Polar Orbiter Data Users Guide’, US Department of Commerce, NOAA, Washington.Google Scholar
  10. Neckel, H. and D. Labs (1984) ‘The solar radiation between 3300 and 12500 A’, Sol. Phys., 90, 205–258.CrossRefGoogle Scholar
  11. Norwood, V. and J. Lansing (1983) ‘Electro-optical imaging sensors’, in R. Colwell (ed.), Manual of Remote Sensing, Vol. 1, American Society of Photogrammetry, Falls Church, 335–367.Google Scholar
  12. Pagano, T. and J. Young (1992) ‘MODIS-N Instrument Status’, Santa Barbara Research Center, Document 92-0257-1, Hughes Corporation.Google Scholar
  13. Rees, W. (1990) ‘Physical Principles of Remote Sensing’, Cambridge University Press, Cambridge 247 pp.Google Scholar
  14. Robinson, B. and D. DeWitt (1983) ‘Electro-optical non-imaging sensors’, in R. Colwell (ed.), Manual of Remote Sensing, Vol. 1, American Society of Photogrammetry, Falls Church, 293–333.Google Scholar
  15. Rozelot, J.-P. (1973) ‘La Couronne Solaire’, Doin, Paris, 144 pp.Google Scholar
  16. Sellers, W. (1965) ‘Physical Climatology’, Chicago University Press, Chicago, 272 pp.Google Scholar
  17. Simonett, D. (1983) ‘The development and principles of remote sensing’, in R. Colwell (ed.), Manual of Remote Sensing, Vol. 1, American Society of Photogrammetry, Falls Church, 1–35.Google Scholar
  18. Slater, P. (1983) ‘Photographic systems for remote sensing’, in R. Colwell, (ed.), Manual of Remote Sensing, Vol. 1, American Society of Photogrammetry, Falls Church, 231–291.Google Scholar
  19. Pinty, B. and M. M. Verstraete (1992) ‘GEMI: A non-linear index to monitor global vegetation from satellites’, Vegetatio, 101, 15–20.CrossRefGoogle Scholar
  20. Vogt, J. (1992) ‘Characterizing the spatio-temporal variability of surface parameters from NOAA AVHRR data: A case for Southern Mali’, Ph.D. Thesis, Trier University, 216 pp.Google Scholar

Copyright information

© ECSC, EEC, EAEC, Brussels and Luxembourg 1994

Authors and Affiliations

  • Michel M. Verstraete
    • 1
  1. 1.Institute for Remote Sensing ApplicationsCommission of the European Communities Joint Research CentreIspra (Va)Italy

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