, Volume 80, Issue 2, pp 71–89 | Cite as

Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux

  • Elgene O. Box
  • Brent N. Holben
  • Virginia Kalb


The Normalized Difference Vegetation Index (NDVI) or ‘greenness index’, based on the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA-7 satellite, has been widely interpreted as a measure of regional to global vegetation patterns. This study provides the first rigorous, quantitative evaluation of global relationships between the NDVI and geographically representative vegetation data-bases, including field metabolic measurements and carbon-balance results from global simulation models. Geographic reliability of the NDVI is judged by comparing NDVI values for different surface types with a general global trend and by statistical analysis of relationships to biomass amounts, net and gross primary productivity, and actual evapotranspiration. NDVI data appear to be relatively reliable predictors of primary productivity except in areas of complex terrain, for seasonal values at high latitudes, and in extreme deserts. The strength of the NDVI-productivity relationship seems comparable to that of earlier climate-based productivity models. Little consistent relationship was found, across different vegetation types, between NDVI and biomass amounts or net biospheric CO2 flux.


Evapotranspiration Field measurement Global geography Greenness index NDVI Radiometer Satellite calibration Simulation data 



Actual Evapotranspiration


Advanced Very High Resolution Radiometer


Gross Primary Production


Global Vegetation Index


Normalized Difference Vegetation Index


Net Primary Production


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  1. AIBS 1986. Special issue on remote sensing of the biosphere. Bio-Science 36: 429–483.Google Scholar
  2. Beard, J. S. 1980. Vegetation survey of Western Australia. 7 vols + maps (7 sheets). Univ. Western Australia Press, Nedlands.Google Scholar
  3. Botkin, D. B., Estes, J. E., MacDonald, R. M. & Wilson, M. V. 1984. Studying the earth's vegetation from space. BioScience 34: 508–514.Google Scholar
  4. Box, E. O. 1978. Geographic dimensions of terrestrial net and gross primary productivity. Radiat. Env. Biophys. 15: 305–322.Google Scholar
  5. Box, E. O. 1979. World map of difference between Thornthwaite and Holdridge PET estimates. In: Harvard Library of Computer Mapping, 5: 11–27 (map 7). Harvard University, Cambridge (Massachusetts).Google Scholar
  6. Box, E. O. 1981. Macroclimate and plant forms: An introduction to predictive modeling in phytogeography. Tasks for Vegetation Science, 1. Junk, The Hague.Google Scholar
  7. Box, E. O. 1982. SOLWAT: A minimal-input soil water simulation system applicable to a full range of natural situations. Univ. of Georgia, Geography Dept., Athens.Google Scholar
  8. Box, E. O. 1986. Some climatic relations of the vegetation of Argentina, in global perspective. In: Eskuche, U. & Landolt, E. (eds), Contributions to Knowledge of the Flora and Vegetation of Northern Argentina, Veröff. Geobotan. Inst. Rübel 18: 181–216.Google Scholar
  9. Box, E. O. 1988. Estimating the seasonal carbon source-sink geography of a natural, steady-state terrestrial biosphere. J. Appl. Meteorol. 27: 1109–1124.Google Scholar
  10. Box, E. O. 1988. Effects of seasonal vegetation structure on carbon dynamics of terrestrial biosphere models. In: Verhoeven, J., et al. (eds), Effect of vegetation structure and composition on carbon and nutrient economy. SPB Academic Publ., The Hague.Google Scholar
  11. Brook, G. A., Folkoff, M. E. & Box, E O., 1983. A world model of soil carbon dioxide. Earth Surface Proc. Landforms 8: 79–88.Google Scholar
  12. Cannell, M. G. R. 1982. World forest biomass and primary production data. Academic Press, London.Google Scholar
  13. Carter, D. B. & Mather, J. R. 1966, Climate classification for environmental biology. Publ. in Climatology, 19(4), Univ. of Delaware.Google Scholar
  14. DeAngelis, D. L., Gardner, R. H. & Shugart, H. H. 1981. Productivity of forest ecosystems studied during the IBP: the woodlands data-set. In: Reichle, D. E. (ed.), Dynamic properties of forest ecosystems, pp. 567–672. Cambridge University Press.Google Scholar
  15. Dwivedi, R. 1971. Suitability of measuring carbon assimilation rates for evaluation of solar energy conservation and drymatter production in plants. Trop. Ecol. 12: 123–132.Google Scholar
  16. Esser, G., Aselmann, I. & Lieth, H. 1982. Modelling the carbon reservoir in the system compartment “Litter”. SCOPE/UNEP special vol. 52, Mitteil. Geol.-Paläont. Inst. Univ. of Hamburg, pp. 39–58.Google Scholar
  17. Eyre, S. R. 1968. Vegetation and soils, 2nd ed. Aldine, Chicago.Google Scholar
  18. Fung, I. Y., Tucker, C. J. & Prentice, K. 1987. Application of advanced very high resolution radiometer vegetation index to study atmosphere-biosphere exchange of CO2. J. Geophys. Res. 92(D3): 2999–3015.Google Scholar
  19. Gillette, D. A. & Box, E. O. 1986. Modeling seasonal changes of atmospheric carbon dioxide and stable carbon isotopes. J. Geophys. Res. 91(D4): 5287–5304.Google Scholar
  20. Goward, S. N., Tucker, C. J. & Dye, D. G. 1985. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio 64: 3–14.Google Scholar
  21. Holben, B. N. 1986. Characteristics of maximum-value composite images of temporal AVHRR data. Int. J. Remote Sens. 7: 1417–1434.Google Scholar
  22. Horvát, I., Glavač, V. & Ellenberg, H. 1974. Vegetation Südosteuropas. Gustav-Fischer-Verlag, Stuttgart.Google Scholar
  23. Hou, Xue-yu et al. 1980. (Vegetation regions of the People's Republic of China). Academia Sinica, Beijing (in Chinese).Google Scholar
  24. Houghton, R. A. 1987a. Biotic changes consistent with the increased seasonal amplitude of atmospheric CO2 concentrations. J. Geophys. Res. 92(D4): 4223–4230.Google Scholar
  25. Houghton, R. A. 1987b. Terrestrial metabolism and atmospheric CO2 concentrations. BioScience 37: 672–678.Google Scholar
  26. Hozumi, K., Yoda, K. & Kira, T. 1969. Production ecology of tropical rainforests in southwestern Cambodia. II. Photosynthetic production in an evergreen seasonal forest. Nature and Life in Southeast Asia, 6: 57–81.Google Scholar
  27. Hueck, K. & Seibert, P. 1972. Vegetationskarte von Südamerika. Vegetationsmonographien der einzelnen Großräume, vol IIa. Gustav-Fischer-Verlag, Stuttgart.Google Scholar
  28. Johannsen, Ch. J. & Sanders, J. L. 1982. Remote sensing for resource management. Soil Conservation Soc. America, Ankeny (Iowa).Google Scholar
  29. Jordan, C. 1971. A world pattern in plant energetics. Amer. Sci. 59: 426–433.Google Scholar
  30. Justice, Ch. O. (ed.) 1986. Monitoring the grasslands of semi-arid Africa using NOAA-AVHRR data. Special number. Int. J. Remote Sensing 7: 1383–1622.Google Scholar
  31. Justice, Ch. O., Townshend, J. R. G., Holben, B. N. & Tucker, C. J. 1985. Analysis of the phenology of global vegetation using meteorological satellite data. Int. J. Remote Sensing 6: 1271–1318.Google Scholar
  32. Kira, T. 1975. Primary production of forests. In: Cooper, J. P. (ed.), Photosynthesis and productivity in different environments. Cambridge Univ. Press.Google Scholar
  33. Küchler, A. W. 1964. The potential natural vegetation of the conterminous United States. Amer. Geogr. Soc., New York.Google Scholar
  34. Kumar, M. & Monteith, J. L., 1981. Remote sensing of crop growth. In: Smith, H. (ed.), Plants and the daylight spectrum. Academic Press, New York.Google Scholar
  35. Lieth, H. 1975a. Primary production of the major vegetation units of the world. In: Lieth, H. & Whittaker, R. H. (eds), Primary productivity of the biosphere, pp. 203–215. Springer-Verlag, New York.Google Scholar
  36. Lieth, H. 1975b. Modeling the primary production of the world. In: Lieth, H. & Whittaker, R. H. (eds), Primary production of the biosphere, pp. 237–263. Springer-Verlag, New York.Google Scholar
  37. Lieth, H. & Box, E. O. 1972. Evapotranspiration and primary productivity: C. W. Thornthwaite Memorial Model. Publ. Climatol. 25: 37–46, University of Delaware.Google Scholar
  38. Lieth, H. & Box, E. O. 1977. The gross primary productivity pattern of the land vegetation: A first attempt. Trop. Ecol. 18: 109–115.Google Scholar
  39. Major, J. 1963. A climatic index to vascular plant activity. Ecology 44: 485–498.Google Scholar
  40. Mather, J. R. 1974. Climatology: fundamentals and applications. McGraw-Hill, New York.Google Scholar
  41. Mather, J. R. & Yoshioka, G. 1966. The role of climate in the distribution of vegetation. Publ. Climatol. 19: 372–384, University of Delaware.Google Scholar
  42. Meentemeyer, V. 1978. Macroclimate and lignin control of litter decomposition rates. Ecology 59: 465–472.Google Scholar
  43. Meentemeyer, V. 1985. Climatic control of litter dynamics in tropical forest. In: Misra, K. C. (ed.), Ecology and resource management in tropics, pp. 302–310, Bhargava, Varanasi (India).Google Scholar
  44. Meentemeyer, V., Box, E. O. & Thompson, R. 1982. World patterns and amounts of terrestrial plant litter production. BioScience 32: 125–128.Google Scholar
  45. Moeller, C. M. 1945. Untersuchungen über Laubmenge, Stoffverlust, und Stoffproduktion des Waldes. Forstl. Forsoegsv. Danmark 17: 1–287.Google Scholar
  46. Moeller, C. M., Mueller, D. & Nielsen, J. 1954. The dry-matter production of the European beech. Forstl. Forsoegsv. Danmark 21: 253–335.Google Scholar
  47. Mueller-Dombois, D. & Ellenberg, H. 1974. Aims and methods of vegetation ecology. Wiley, New York.Google Scholar
  48. Odum, E. P. 1971. Fundamentals of ecology, 3rd ed. W. B. Saunders Co., Philadelphia.Google Scholar
  49. Olson, J. 1963. Energy storage and the balance of producers and decomposers in ecological systems., Ecology 44: 322–331.Google Scholar
  50. O'Neill, R. V. & DeAngelis, D. L. 1981. Comparative productivity and biomass relations of forest ecosystems. In: Reichle, D. E. (ed.), Dynamic properties of forest ecosystems, pp. 411–449. IBP Series, Vol. 23. Cambridge University Press.Google Scholar
  51. Rosenzweig, M. L. 1968. Net primary productivity of terrestrial communities: prediction from climatological data. Amer. Nat. 102: 67–74.Google Scholar
  52. Rowe, J. S. 1972. Forest regions of Canada, revised ed. Dept. of Natural Resources, Ottawa.Google Scholar
  53. Salisbury, F. B. & Ross, C. W. 1978. Plant physiology. 2nd ed. Wadsworth Publ., Belmont (Calif.).Google Scholar
  54. SAS 1986. Statistical Analysis System, version 5.16, SAS Institute, Cary (North Carolina).Google Scholar
  55. Schmithüsen, J. 1976. Atlas zur Biogeographie. Bibliographisches Institut, Mannheim.Google Scholar
  56. Sharp, D. D., Wyatt, R. E. & Lieth, H. 1974. Length of the growing period, in months (world map) In: Lieth, H. (ed.), Phenology and seasonality modeling. Springer-Verlag, New York.Google Scholar
  57. Sharp, D. D., Lieth, H., Noggle, G. R. & Gross, H. D. 1976. Agricultural and forest primary productivity in North Carolina 1972–1973. N. Carolina Agric. Expt. Station. Techn. Bull. 241, Raleigh.Google Scholar
  58. Shidei, T. & Kira, T. (eds) 1977. Primary productivity of Japanese forests. JIBP Synthesis series, Vol. 16, Tokyo.Google Scholar
  59. Shugart, H. H. 1984. A theory of forest dynamics. Springer-Verlag, New York.Google Scholar
  60. Slota, R. P. 1985. Climatic control of leaf litter production. M. A. thesis. University of Georgia, Athens (Georgia).Google Scholar
  61. Tarpley, J. D., Schneider, S. R. & Money, R. L. 1984. Global vegetation indices from the NOAA-7 meteorological satellite. J. Clim. Appl. Meteorol. 23: 491–494.Google Scholar
  62. Thomas, G. & Henderson-Sellers, A. 1987. Evaluation of satellite-derived land cover characteristics for global climate modelling. Climatic Change 11: 313–347.Google Scholar
  63. Townshend, J. R. G. & Tucker, C. J. 1984. Objective assessment of advanced very high resolution radiometer data for land cover mapping. Int. J. Remote Sens. 5: 492–504.Google Scholar
  64. Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127–150.Google Scholar
  65. Tucker, C. J. 1980. A critical review of remote sensing and other methods for non-destructive estimation of standing crop biomass. Grass Forage Science 35: 177–182.Google Scholar
  66. Tucker, C. J., Townshend, J. R. G. & Goff, T. E. 1985a. African landcover classification using satellite data. Science 227: 369–375.Google Scholar
  67. Tucker, C. J. Vanpraet, C. L., Sharman, M. J. & Ittersum, G. 1985b. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984. Remote Sens. Environ. 17: 233–249.Google Scholar
  68. Tucker, C. J., Fung, I. Y., Keeling, C. D. & Gammon, R. H. 1986. Relationship between atmospheric CO2 variations and a satellite-derived vegetation index. Nature 319: 195–199.Google Scholar
  69. Tucker, C. J. & Sellers, P. J. 1986. Satellite remote sensing of primary production. Int. J. Remote Sensing, 7: 1395–1416.Google Scholar
  70. Turner, M. G. 1987. Land-use changes and net primary production in the Georgia (USA) landscape, 1935–1982. Environ. Management 11: 237–247.Google Scholar
  71. UNESCO 1969. Vegetation map of the Mediterranean zone. UNESCO, Paris.Google Scholar
  72. UNESCO 1981a. Vegetation map of Africa. UNESCO, Paris.Google Scholar
  73. Walter, H. 1968 Die Vegetation der Erde in öko-physiologischer Betrachtung, Vol. 2: Die gemäßigten und arktischen Zonen. VEB Gustav-Fischer-Verlag, Jena.Google Scholar
  74. Walter, H. 1973. Die Vegetation der Erde in öko-physiologischer Betrachtung, Vol. 1: Die tropischen und subtropischen Zonen, 3rd ed. Gustav-Fischer-Verlag, Stuttgart.Google Scholar
  75. Walter, H. 1984. Vegetation of the earth. 3rd ed. Springer-Verlag, New York.Google Scholar
  76. Waring, R. H., Emmingham, W. H., Gholz, H. L. & Grier, C. C. 1978. Variation in maximum leaf area of coniferous forests in Oregon and its ecological significance. Forest Science 24: 131–140.Google Scholar
  77. Waring, R. H. & Schlesinger, W. H. 1985. Forest ecosystems: concepts and management. Academic Press, Orlando (Florida).Google Scholar
  78. Whitford, W. G., Meentemeyer, V., Seastedt, T. R., Cromack, K., Crossley, D. A.Jr, Santos, P., Todd, R. L. & Waide, J. B. 1981. Exceptions to the AET model: deserts and clear-cut forest. Ecology 62: 275–277.Google Scholar

Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • Elgene O. Box
    • 1
  • Brent N. Holben
    • 2
  • Virginia Kalb
    • 2
  1. 1.Geography DepartmentUniversity of GeorgiaAthensUSA
  2. 2.National Aeronautics and Space AdministrationGoddard Space Flight CenterGreenbeltUSA

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