Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System

  • Steven W. Running
  • Peter E. Thornton
  • Ramakrishna Nemani
  • Joseph M. Glassy


Probably the single most fundamental measure of “global change” of highest practical interest to humankind is the change in terrestrial biological productivity. Biological productivity is the source of all the food, fiber, and fuel by which humans survive, and so defines most fundamentally the habitability of Earth. The spatial variability of net primary productivity (NPP) over the globe is enormous, from about 1000 g Cm-2 for evergreen tropical rain forests to less than 30 g Cm-2 for deserts (Scurlock et al. 1999). With increased atmospheric carbon dioxide (CO2) and global climate change, NPP over large areas may be changing (Myneni et al. 1997a, VEMAP 1995, Melillo et al. 1993). Understanding regional variability in carbon cycle processes requires a more spatially detailed analysis of global land surface processes. Since December 1999, the U.S. National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) produces a regular global estimate of (gross primary productivity, GPP) and annual NPP of the entire terrestrial earth surface at 1-km spatial resolution, 150 million cells, each having GPP and NPP computed individually.


Normalize Difference Vegetation Index Absorb Photosynthetically Active Radiation Earth Observe System Global Terrestrial Live Wood 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Asrar, G.; Myneni, R.; Choudhury, B.J. Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study. Remote Sens. Environ. 41:85–103; 1992.CrossRefGoogle Scholar
  2. Baldocchi, D.; Valentini, R.; Running, S.W.; Oechel, W.; Dahlman, R. Strategies for measuring and modelling carbon dioxide and water vapor fluxes over terrestrial ecosystems. Global Change Biol. 2:159–168; 1996.CrossRefGoogle Scholar
  3. Bondeau, A.; Kicklighter, J.; Kaduk, J.; and participants of the Potsdam NPP Model Intercomparison. Comparing global models of terrestrial net primary productivity (NPP): Importance of vegetation structure on seasonal NPP estimates. Global Change Biology 5:35–45; 1999.CrossRefGoogle Scholar
  4. Cannell, M.G.R. World Forest Biomass and Primary Production Data. London: Academic; 1982.Google Scholar
  5. Churkina, G.; Running, S.W. Contrasting climatic controls on the estimated productivity of different biomes. Ecosystems 1:206–215; 1998.CrossRefGoogle Scholar
  6. Churkina, G.; Running, S.W.; Schloss, A.L.; PIK-NPP Participants. Comparing global models of terrestrial net primary productivity (NPP): The importance of water availability. Global Change Biol. 5:46–55; 1999.CrossRefGoogle Scholar
  7. Ciais, P., Tans, P.P., Trolier, M., White, J.W.C., and Francey, R.J. A large northern hemisphere terrestrial CO2 sink indicated by 13C/12C of atmospheric CO2. Science 269:1098–1102; 1995.PubMedCrossRefGoogle Scholar
  8. Cramer, W.; Field, C.B. Comparing global models of terrestrial net primary productivity (NPP): Introduction. Global Change Biol. 5:iii–iv; 1999.CrossRefGoogle Scholar
  9. DeFries, R.S.; Townshend, J.R.G.; Hansen, M.C. Continuous fields of vegetation characteristics at the global scale at 1 km resolution. J. Geophys. Res. Atmos. 104(D14):16911–16924; 1999.CrossRefGoogle Scholar
  10. Field, C.B.; Behrenfeld, M.J.; Randerson, J.T.; Falkowski, P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281:237–240; 1998.PubMedCrossRefGoogle Scholar
  11. Field, C.B.; Randerson, J.T.; Malmstrom, C.M. Global net primary production: Combining ecology and remote sensing. Remote Sens. Environ. 51:74–88; 1995.CrossRefGoogle Scholar
  12. Goulden, M.L., Munger, J.W., Fan, S-M., Daube, B.C., and Wofsy, S.C. Exchange of carbon dioxide by a deciduous forest: Response to interannual climate variability. Science 271:1576–1578; 1996.CrossRefGoogle Scholar
  13. DeFries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Sohlberg, R. Global land cover classification at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Internat. J. Remote Sens. 19(16):3141–3168.Google Scholar
  14. Hudson, R.J.M.; Gherini, S.A.; Goldstein, R.A. Modeling the global carbon cycle: Nitrogen fertilization of the terrestrial biosphere and the “missing” CO2 sink. Global Biogeochem. Cycl. 8(3):307–333; 1994.CrossRefGoogle Scholar
  15. Hunt, E.R., Jr. Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDVI. Internat. J. Remote Sens. 15:1725–1730; 1994.CrossRefGoogle Scholar
  16. Hunt, E.R., Jr., Piper, S.C.; Nemani, R.R.; Keeling, C.D.; Otto, R.D.; Running, S.W. Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model. Global Biogeochem. Cycl. 10:431–456; 1996.CrossRefGoogle Scholar
  17. Justice, C.O.; Running, S.W.; et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 36(4): 1228–1249; 1998.CrossRefGoogle Scholar
  18. Keeling, C.D.; Chin, J.F.S.; Whorf, T.P. Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature 382:146–149; 1996.CrossRefGoogle Scholar
  19. Kicklighter, D.W.; Bondeau, A.; Schloss, A.L.; Kaduk, J.; McGuire, A.D.; and participants of the Potsdam NPP Model Intercomparison. Comparing global models of terrestrial net primary productivity (NPP): Global pattern and differentiation by major bidmes. Global Change Biol. 5:16–24; 1999.CrossRefGoogle Scholar
  20. Kimball, J.; Running, S.W.; Nemani, R.R. An improved method for estimating surface humidity from daily minimum temperature. Agric. For. Meteorol. 85:87–98; 1997a.CrossRefGoogle Scholar
  21. Kimball, J.S.; Thornton, P.E.; White, M.A.; Running, S.W. Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region. Tree Physiol. 17:589–599; 1997b.PubMedCrossRefGoogle Scholar
  22. Kimball, J.S.; White, M.A.; Running, S.W. BIOME-BGC simulations of stand hydrologic processes for BOREAS. J. Geophys. Res. 102(D24):29043–29051: 1997c.CrossRefGoogle Scholar
  23. Landsberg, J.J.; Prince, S.D.; Jarvis, P.G.; McMurtrie, R.E.; Luxmoore, R.; Medlyn, B.E. Energy conversion and use in forests: The analysis of forest production in terms of radiation utilization efficiency. In: Gholz, H.L.; Nakane, K., eds. The Use of Remote Sensing in the Modeling of Forest Productivity at Scales from the Stand to the Globe. London: Kluwer; 1996.Google Scholar
  24. Larcher, W. Physiological Plant Ecology. Springer-Verlag, Berlin; 1995.CrossRefGoogle Scholar
  25. Maier, C.A.; Zarnoch, S.J.; Dougherty, P.M. Effects of temperature and tissue nitrogen on dormant season stem and branch maintenance respiration in a young loblolly pine (Pinus taeda) plantation. Tree Physiol. 18:11–20; 1998.PubMedCrossRefGoogle Scholar
  26. Melillo, J.M.; McGuire, A.D.; Kicklighter, D.W.; Moore, B., III; Vorosmarty, C.J.; Schloss, A.L. Global climate change and terrestrial net primary production. Nature 363:234–240; 1993.CrossRefGoogle Scholar
  27. Monteith, J.L. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9:747–766; 1972.CrossRefGoogle Scholar
  28. Monteith, J.L. Climate and the efficiency of crop production in Britain. Philosoph. Trans. R Soc. Lond. 281:277–294; 1977.CrossRefGoogle Scholar
  29. Myneni, R.B.; Keeling, C.D.; Tucker, C.J.; Asrar, G.; Nemani, R.R. Increased plant growth in the northern high latitudes between 1981–1991. Nature 386:698–702; 1997a.CrossRefGoogle Scholar
  30. Myneni, R.B.; Nemani, R.R.; Running, S.W. Estimation of global LAI and FPAR from radiative transfer models. IEEE Trans. Geosci. Remote Sens. 35:1380–1393; 1997b.CrossRefGoogle Scholar
  31. Nemani, R.R.; Running, S.W. Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation. Agric. For. Meteorol. 44:245–260; 1989.CrossRefGoogle Scholar
  32. Nemani, R.R.; Running, S.W. Implementation of a hierarchical global vegetation classification in ecosystem function models. J. Vegetat. Sci. 7:337–346; 1996.CrossRefGoogle Scholar
  33. Nemani, R.R.; Running, S.W. Land cover characterization using multi-temporal red, NIR and thermal-IR AVHRR data. Ecol. Applic. 7:79–90; 1997.CrossRefGoogle Scholar
  34. Nemery, B.; Francois, L.; Gerard, J.C.; Bondeau, A.; Heimann, H.; and participants of the Potsdam NPP Model Intercomparison. Comparing global models of terrestrial net primary productivity (NPP): Analysis of the seasonal atmospheric CO2 signal. Global Change Biol. 5:65–76; 1999.CrossRefGoogle Scholar
  35. Piper, S.C.; Stewart, E.F. A gridded global data set of daily temperature and precipitation for terrestrial biospheric modeling. Global Biogeochem. Cycl. 10:757–782; 1996.CrossRefGoogle Scholar
  36. Prince, S.D. A model of regional primary production for use with coarse resolution satellite data. Internat. J. Remote Sens. 12:1313–1330; 1991.CrossRefGoogle Scholar
  37. Prince, S.D.; Goward, S.N. Global primary production: A remote sensing approach. J. Biogeogr. 22:815–835; 1995.CrossRefGoogle Scholar
  38. Randerson, J.T.; Thompson, M.V.; Conway, T.J.; Fung, I.Y.; Field, C.B. The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide. Global Biogeochem. Cycl. 11:535–560; 1997.CrossRefGoogle Scholar
  39. Ruimy, A.; Saugier, B. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. 99:5263–5283; 1994.CrossRefGoogle Scholar
  40. Running, S.W.; Baldocchi, D.D.; Turner, D.P.; Gower, S.T.; Bakwin, P.S.; Hibbard, K.A. A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens. Environ. 70(1): 108–127; 1999.CrossRefGoogle Scholar
  41. Running, S.W.; Gower, S.T. FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiol. 9:147–160; 1991.PubMedGoogle Scholar
  42. Running, S.W.; Hunt, Jr., E.R. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer, J.R.; Field, C., eds. Scaling Physiological Processes: Leaf to Globe. Orlando, FL: Academic; 1993.Google Scholar
  43. Running, S.W.; Justice, C.; Salomonson, V.; Hall, D.; Barker, J.; Kaufmann, Y.; Strahler, A.; Huete, A.; Muller, J.P.; Vanderbilt, V.; Wan, Z.M.; Teillet, P.; Carneggie, D. Terrestrial remote sensing science and algorithms planned from EOS/MODIS. Internat. J. Remote Sens. 15:358–3620; 1994.Google Scholar
  44. Running, S.W.; Loveland, T.R.; Pierce, L.L.; Nemani, R.R.; Hunt Jr., E.R. A remote sensing based vegetation classification logic for global land cover analysis. Remote Sens. Environ. 51:39–48; 1995.CrossRefGoogle Scholar
  45. Ryan, M.G.; Binkley, D.; Fownes, J.H. Age-related decline in forest productivity: Pattern and Process. Adv. Ecol. Res. 27:213–261; 1997.CrossRefGoogle Scholar
  46. Schloss, A.L.; Kicklighter, D.W.; Kaduk, J.; Wittenberg, U.; and the participants of the Potsdam NPP Model Intercomparison. Comparing global models of terrestrial net primary productivity (NPP): Comparison of NPP to climate and the Normalized Difference Vegetation Index (NDVI). Global Change Biol. 5:25–34; 1999.CrossRefGoogle Scholar
  47. Schulze, E.D.; Kelliher, F.M.; Korner, C.; Loyd, J.; Leuing, R. Relationships among maximum stomatal conductance, ecosystem surface conductance, carbon assimilation rate and plant nitrogen nutrition. Annu. Rev. Ecol. Syst. 25:629–660; 1994.CrossRefGoogle Scholar
  48. Scurlock, J.M.O.; Cramer, W.; Olson, R.J.; Parton, W.J.; Prince, S.D. Terrestrial NPP: Towards a consistent data set for global model evaluation. Ecol. Applic. 9(3):913–919; 1999.Google Scholar
  49. Sellers, P.J. Canopy reflectance, photosynthesis and transpiration. II. The role of biophysics in the linearity of their interdependence. Remote Sens. Environ. 21:143–183; 1987.CrossRefGoogle Scholar
  50. Sprugel, D.G.; Ryan, M.G.; Brooks, J.R.; Vogt, K.A.; Martin, T.A. Respiration from the organ level to stand level. In Smith, W.K.; Hinkley, T.M., eds. Resource Physiology of Conifers. San Diego, CA: Academic; 1995:255–299.Google Scholar
  51. Tans, P.P.; Fung, I.Y.; Takahashi, T. Observational constraints on the global atmospheric CO2 budget. Science 247:1431–1438; 1990.PubMedCrossRefGoogle Scholar
  52. Thornton, P.E.; Running, S.W. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric. For. Meteorol. 93:211–228; 1999.CrossRefGoogle Scholar
  53. VEMAP [Vegetation/Ecosystem Modeling and Analysis Project] Members. Comparing biogeography and bio-geochemistry models in a continental scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Global Biogeochem. Cycl. 9:407–437; 1995.CrossRefGoogle Scholar
  54. Waring, R.H.; Law, B.; et al. Scaling gross ecosystem production at Harvard Forest with remote sensing: A comparison of estimates from a constrained quantum-use efficiency model and eddy correlation. Plant Cell Environ. 18:1201–1213; 1995.CrossRefGoogle Scholar
  55. Waring, R.; Running, S.W. Forest Ecosystems: Analysis at Multiple Scales. San Diego, CA: Academic; 1998.Google Scholar
  56. White, M.E.; Thornton, P.E.; Running, S.W. A continental phenology model for monitoring vegetation responses to inter-annual climatic variability. global Biogeochem. Cycl. 11(2):217–234; 1997.CrossRefGoogle Scholar
  57. Zobler, L.A. A World Soil File for Global Climate Modeling. Tech. Memo. 87802. U.S. National Aeronautics and Space Administration (NASA), Greenbelt, MD; 1986.Google Scholar

Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Steven W. Running
  • Peter E. Thornton
  • Ramakrishna Nemani
  • Joseph M. Glassy

There are no affiliations available

Personalised recommendations