Land Surface Phenology

Convergence of Satellite and CO2 Eddy Flux Observations
  • Xiangming Xiao
  • Junhui Zhang
  • Huimin Yan
  • Weixing Wu
  • Chandrashekhar Biradar


Land surface phenology (LSP) is a key indicator of ecosystem dynamics under a changing environment. Over the last few decades, numerous studies have used the time series data of vegetation indices derived from land surface reflectance acquired by satellite-based optical sensors to delineate land surface phenology. Recent progress and data accumulation from CO2 eddy flux towers offers a new perspective for delineating land surface phenology through either net ecosystem exchange of CO2 (NEE) or gross primary production (GPP). In this chapter, we discussed the potential convergence of satellite observation approach and CO2 eddy flux observation approach. We evaluated three vegetation indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Land Surface Water Index) in relation to NEE and GPP data from five CO2 eddy flux tower sites, representing five vegetation types (deciduous broadleaf forests, evergreen needleleaf forest, temperate grassland, cropland, and tropical moist evergreen broadleaf forest). This chapter highlights the need for the community to combine satellite observation approach and CO2 eddy flux observation approach, in order to develop better understanding of land surface phenology.



This study was supported by NASA Land Cover and Land Use Change Program (the Northern Eurasia Earth Science Partnership Initiative (NEESPI); NN-H-04-Z-YS-005-N, and NNG05GH80G), and NASA Interdisciplinary Science program (NAG5-11160, NAG5-10135), and National Key Research and Development Program of China ( 2002CG412501) and International Partnership Project of Chinese Academy of Sciences ( CXTD-Z2005-1).


  1. Baldocchi, D., Falge, E., Gu, L.H., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bern-hofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X.H., Malhi, Y., Meyers, T., Munger, W., Oechel, W., U, K.T.P., Pilegaard, K., Schmid, H.P., Valentini, R., Verma, S., Vesala, T., Wilson, K. and Wofsy, S. (2001) FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434.CrossRefGoogle Scholar
  2. Baldocchi, D., Valentini, R., Running, S., Oechel, W. and Dahlman, R. (1996) Strategies for measuring and modelling carbon dioxide and water vapour fluxes over terrestrial ecosys-tems. Global Change Biol. 2, 159–168.CrossRefGoogle Scholar
  3. Baldocchi, D. and Wilson, K. (2001) Modeling CO2 and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales. Ecol. Modell. 142, 155–184.CrossRefGoogle Scholar
  4. Barford, C.C., Wofsy, S.C., Goulden, M.L., Munger, J.W., Pyle, E.H., Urbanski, S.P., Hutyra, L., Saleska, S.R., Fitzjarrald, D. and Moore, K. (2001) Factors controlling long- and short-term sequestration of atmospheric CO2 in a mid-latitude forest. Science 294, 1688–1691.CrossRefGoogle Scholar
  5. Boegh, E., Soegaard, H., Broge, N., Hasager, C.B., Jensen, N.O., Schelde, K. and Thomsen, A. (2002) Airborne multispectral data for quantifying leaf area index, nitrogen concentra-tion, and photosynthetic efficiency in agriculture. Remote Sens. Environ. 81, 179–193.CrossRefGoogle Scholar
  6. Boles, S.H., Xiao, X.M., Liu, J.Y., Zhang, Q.Y., Munkhtuya, S., Chen, S.Q. and Ojima, D. (2004) Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sens. Environ. 90, 477–489.CrossRefGoogle Scholar
  7. Ceccato, P., Flasse, S. and Gregoire, J.M. (2002a) Designing a spectral index to estimate vegetation water content from remote sensing data – Part 2. Validation and applications. Remote Sens. Environ. 82, 198–207.CrossRefGoogle Scholar
  8. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S. and Gregoire, J.M. (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Envi-ron. 77, 22–33.CrossRefGoogle Scholar
  9. Ceccato, P., Gobron, N., Flasse, S., Pinty, B. and Tarantola, S. (2002b) Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 – Theoretical approach. Remote Sens. Environ. 82, 188–197.CrossRefGoogle Scholar
  10. Churkina, G., Schimel, D., Braswell, B.H. and Xiao, X.M. (2005) Spatial analysis of growing season length control over net ecosystem exchange. Global Change Biol. 11, 1777–1787.CrossRefGoogle Scholar
  11. Delbart, N., Kergoat, L., Le Toan, T., Lhermitte, J. and Picard, G. (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens. Environ. 97, 26–38.CrossRefGoogle Scholar
  12. Falge, E., Baldocchi, D., Tenhunen, J., Aubinet, M., Bakwin, P., Berbigier, P., Bernhofer, C., Burba, G., Clement, R., Davis, K.J., Elbers, J.A., Goldstein, A.H., Grelle, A., Granier, A., Guomundsson, J., Hollinger, D., Kowalski, A.S., Katul, G., Law, B.E., Malhi, Y., Meyers, T., Monson, R.K., Munger, J.W., Oechel, W., Paw, K.T., Pilegaard, K., Rannik, U., Reb-mann, C., Suyker, A., Valentini, R., Wilson, K. and Wofsy, S. (2002) Seasonality of eco-system respiration and gross primary production as derived from FLUXNET measure-ments. Agric. For. Meteorol. 113, 53–74.CrossRefGoogle Scholar
  13. Fu, Y.L., Yu, G.R., Sun, X.M., Li, Y.N., Wen, X.F., Zhang, L.M., Li, Z.Q., Zhao, L. and Hao, Y.B. (2006a) Depression of net ecosystem CO2 exchange in semi-arid Leymus chinensis steppe and alpine shrub. Agric. For. Meteorol. 137, 234–244.CrossRefGoogle Scholar
  14. Fu, Y.L., Yu, G.R., Wang, Y.F., Li, Z.Q. and Hao, Y.B. (2006b) Effect of water stress on ecosystem photosynthesis and respiration of a Leymus chinensis steppe in Inner Mongolia. Sci. China D 49, 196–206.CrossRefGoogle Scholar
  15. Gao, B.C. (1996) NDWI – A normalized difference water index for remote sensing of vegeta-tion liquid water from space. Remote Sens. Environ. 58, 257–266.CrossRefGoogle Scholar
  16. Gao, X., Huete, A.R., Ni, W.G. and Miura, T. (2000) Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sens. Environ. 74, 609–620.CrossRefGoogle Scholar
  17. Goulden, M.L., Munger, J.W., Fan, S.M., Daube, B.C. and Wofsy, S.C. (1996) Exchange of carbon dioxide by a deciduous forest: Response to interannual climate variability. Science 271, 1576–1578.CrossRefGoogle Scholar
  18. Goward, S.N., Markham, B., Dye, D.G., Dulaney, W. and Yang, J.L. (1991) Normalized Difference Vegetation Index measurements from the Advanced Very High-Resolution Ra-diometer. Remote Sens. Environ. 35, 257–277.CrossRefGoogle Scholar
  19. Hollinger, D., Aber, J., Dail, B., Davidson, E.A., Goltz, S.M., Hughes, H., Leclerc, M.Y., Lee, J.T., Richardson, A.D., Rodrigues, C., Scott, N.A., Achuatavarier, D. and Walsh, J. (2004) Spatial and temporal variability in forest-atmosphere CO2 exchange. Global Change Biol. 10, 1689–1706.CrossRefGoogle Scholar
  20. Hollinger, D.Y., Goltz, S.M., Davidson, E.A., Lee, J.T., Tu, K. and Valentine, H.T. (1999) Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest. Global Change Biol. 5, 891–902.CrossRefGoogle Scholar
  21. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213.CrossRefGoogle Scholar
  22. Huete, A.R., Didan, K., Shimabukuro, Y.E., Ratana, P., Saleska, S.R., Hutyra, L.R., Yang, W.Z., Nemani, R.R. and Myneni, R. (2006) Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405, doi:10.1029/2005GL025583.CrossRefGoogle Scholar
  23. Huete, A.R., Liu, H.Q., Batchily, K. and vanLeeuwen, W. (1997) A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59, 440–451.CrossRefGoogle Scholar
  24. Hunt, E.R. and Rock, B.N. (1989) Detection of changes in leaf water-content using near-infrared and middle-infrared reflectances. Remote Sens. Environ. 30, 43–54.CrossRefGoogle Scholar
  25. Hunt, E.R., Rock, B.N. and Nobel, P.S. (1987) Measurement of leaf relative water-content by infrared reflectance. Remote Sens. Environ. 22, 429–435.CrossRefGoogle Scholar
  26. Jenkins, J.P., Braswell, B.H., Frolking, S.E. and Aber, J.D. (2002) Detecting and predicting spatial and interannual patterns of temperate forest springtime phenology in the eastern US. Geophys. Res. Lett. 29, 2201, doi:10.1029/2001GL014008.CrossRefGoogle Scholar
  27. Justice, C.O., Vermote, E., Townshend, J.R.G., Defries, R., Roy, D.P., Hall, D.K., Salomon-son, V.V., Privette, J.L., Riggs, G., Strahler, A., Lucht, W., Myneni, R.B., Knyazikhin, Y., Running, S.W., Nemani, R.R., Wan, Z.M., Huete, A.R., van Leeuwen, W., Wolfe, R.E., Giglio, L., Muller, J.P., Lewis, P. and Barnsley, M.J. (1998) The Moderate Resolution Im-aging Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Trans. Geosci. Rem. Sens. 36, 1228–1249.CrossRefGoogle Scholar
  28. Knyazikhin, Y., Martonchik, J.V., Myneni, R.B., Diner, D.J. and Running, S.W. (1998) Syn-ergistic algorithm for estimating vegetation canopy leaf area index and fraction of ab-sorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res. D103, 32257–32275.CrossRefGoogle Scholar
  29. Li, J., Yu, Q., Sun, X., Tong, X., Ren, C., Wang, J., Liu, E., Zhu, Z. and Yu, G. (2006) Carbon dioxide exchange and the mechanism of environmental control in a farmland ecosystem in North China Plain. Sci. China D 46, 226–240.CrossRefGoogle Scholar
  30. Lieth, H. (Ed.) (1974) Phenology and Seasonality Modeling. Springer, New York, pp. 444.Google Scholar
  31. Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Ne-mani, R.R. and Running, S.W. (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231.CrossRefGoogle Scholar
  32. Myneni, R.B., Keeling, C.D., Tucker, C.J., Asrar, G. and Nemani, R.R. (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702.CrossRefGoogle Scholar
  33. Myneni, R.B., Tucker, C.J., Asrar, G. and Keeling, C.D. (1998) Interannual variations in satellite-sensed vegetation index data from 1981 to 1991. J. Geophys. Res. D103, 6145–6160.CrossRefGoogle Scholar
  34. Myneni, R.B. and Williams, D.L. (1994) On the relationship between fAPAR and NDVI. Remote Sens. Environ. 49, 200–211.CrossRefGoogle Scholar
  35. Philippon, N., Jarlan, L., Martiny, N., Camberlin, P. and Mougin, E. (2007) Characterization of the interannual and intraseasonal variability of West African vegetation between 1982 and 2002 by means of NOAA AVHRR NDVI data. J. Clim. 20, 1202–1218.CrossRefGoogle Scholar
  36. Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A. and Klooster, S.A. (1993) Terrestrial ecosystem production – a process model-based on global satellite and surface data. Global Biogeochem. Cycles 7, 811–841.CrossRefGoogle Scholar
  37. Prince, S.D. and Goward, S.N. (1995) Global primary production: A remote sensing approach. J. Biogeogr. 22, 815–835.CrossRefGoogle Scholar
  38. Richardson, A.D., Bailey, A.S., Denny, E.G., Martin, C.W. and O’Keefe, J. (2006) Phenology of a northern hardwood forest canopy. Global Change Biol. 12, 1174–1188.CrossRefGoogle Scholar
  39. Roberts, D.A., Dennison, P.E., Gardner, M.E., Hetzel, Y., Ustin, S.L. and Lee, C.T. (2003) Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer. IEEE Trans. Geosci. Rem. Sens. 41, 1297–1310.CrossRefGoogle Scholar
  40. Ruimy, A., Saugier, B. and Dedieu, G. (1994) Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. D99, 5263–5283.CrossRefGoogle Scholar
  41. Sakamoto, T., Van Nguyen, N., Kotera, A., Ohno, H., Ishitsuka, N. and Yokozawa, M. (2007) Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong delta from MODIS time-series imagery. Remote Sens. Environ. 109, 295–313.CrossRefGoogle Scholar
  42. Saleska, S.R., Miller, S.D., Matross, D.M., Goulden, M.L., Wofsy, S.C., da Rocha, H.R., de Camargo, P.B., Crill, P., Daube, B.C., de Freitas, H.C., Hutyra, L., Keller, M., Kirchhoff, V., Menton, M., Munger, J.W., Pyle, E.H., Rice, A.H. and Silva, H. (2003) Carbon in amazon forests: Unexpected seasonal fluxes and disturbance-induced losses. Science 302, 1554–1557.CrossRefGoogle Scholar
  43. Schwartz, M.D. (Ed.) (2003) Phenology: An Integrative Environmental Science. Kluwer, Dordrecht, The Netherlands, pp. 592.Google Scholar
  44. Serrano, L., Ustin, S.L., Roberts, D.A., Gamon, J.A. and Penuelas, J. (2000) Deriving water content of chaparral vegetation from AVIRIS data. Remote Sens. Environ. 74, 570–581.CrossRefGoogle Scholar
  45. Shaw, R.H., Silversides, R.H. and Thurtell, G.W. (1974) Some observations of turbulence and turbulent transport within and above plant canopies. Bound.-Lay. Meteorol. 5, 429–449.CrossRefGoogle Scholar
  46. Stockli, R. and Vidale, P.L. (2004) European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int. J. Remote Sens. 25, 3303–3330.CrossRefGoogle Scholar
  47. Studer, S., Stockli, R., Appenzeller, C. and Vidale, P.L. (2007) A comparative study of satel-lite and ground-based phenology. Int. J. Biometeorol. 51, 405–414.CrossRefGoogle Scholar
  48. Tucker, C.J. (1979) Red and photographic infrared linear combinations for monitoring vegeta-tion. Remote Sens. Environ. 8, 127–150.CrossRefGoogle Scholar
  49. Van Schaik, C.P., Terborgh, J.W. and Wright, S.J. (1993) The phenology of tropical forests – adaptive significance and consequences for primary consumers. Ann. Rev. Ecol. Syst. 24, 353–377.CrossRefGoogle Scholar
  50. Vermote, E.F. and Vermeulen, A. (1999) Atmospheric correction algorithm: Spectral reflec-tance (MOD09), MODIS Algorithm Technical Background Document, version 4.0University of Maryland, Department of Geography, pp.107.Google Scholar
  51. Waring, R.H., Law, B.E., Goulden, M.L., Bassow, S.L., Mccreight, R.W., Wofsy, S.C. and Bazzaz, F.A. (1995) 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.CrossRefGoogle Scholar
  52. White, M.A., Hoffman, F., Hargrove, W.W. and Nemani, R.R. (2005) A global framework for monitoring phenological responses to climate change. Geophys. Res. Lett. 32, L04705.CrossRefGoogle Scholar
  53. White, M.A. and Nemani, A.R. (2003) Canopy duration has little influence on annual carbon storage in the deciduous broad leaf forest. Global Change Biol. 9, 967–972.CrossRefGoogle Scholar
  54. White, M.A. and Nemani, R.R. (2006) Real-time monitoring and short-term forecasting of land surface phenology. Remote Sens. Environ. 104, 43–49.CrossRefGoogle Scholar
  55. White, M.A., Running, S.W. and Thornton, P.E. (1999) The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest. Int. J. Biometeorol. 42, 139–145.CrossRefGoogle Scholar
  56. White, M.A., Thornton, P.E. and Running, S.W. (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem. Cycles 11, 217–234.CrossRefGoogle Scholar
  57. Wofsy, S.C., Goulden, M.L., Munger, J.W., Fan, S.M., Bakwin, P.S., Daube, B.C., Bassow, S.L. and Bazzaz, F.A. (1993) Net Exchange of CO2 in a mid-latitude Forest. Science 260, 1314–1317.CrossRefGoogle Scholar
  58. Wright, S.J. and van Schaik, C.P. (1994) Light and the phenology of tropical trees. The Am. Nat. 143, 192–199.CrossRefGoogle Scholar
  59. Xiao, X., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L. and Zhao, R. (2002a) Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. Int. J. Remote Sens. 23, 3009–3022.CrossRefGoogle Scholar
  60. Xiao, X., Boles, S., Liu, J.Y., Zhuang, D.F. and Liu, M.L. (2002b) Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sens. Environ. 82, 335–348.CrossRefGoogle Scholar
  61. Xiao, X., Shu, J., Wang, Y., Ojima, D. and Bonham, C. (1996) Temporal variation in above-ground biomass of Leymus chinense steppe from species to community levels in the Xilin River basin, Inner Mongolia, China. Vegetation 123, 1–12.CrossRefGoogle Scholar
  62. Xiao, X.M., Braswell, B., Zhang, Q.Y., Boles, S., Frolking, S. and Moore, B. (2003) Sensitiv-ity of vegetation indices to atmospheric aerosols: continental-scale observations in North-ern Asia. Remote Sens. Environ. 84, 385–392.CrossRefGoogle Scholar
  63. Xiao, X.M., Hagen, S., Zhang, Q.Y., Keller, M. and Moore, B. (2006) Detecting leaf phenol-ogy of seasonally moist tropical forests in South America with multi-temporal MODIS images. Remote Sens. Environ. 103, 465–473.CrossRefGoogle Scholar
  64. Xiao, X.M., Hollinger, D., Aber, J., Goltz, M., Davidson, E.A., Zhang, Q.Y. and Moore, B. (2004a) Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89, 519–534.CrossRefGoogle Scholar
  65. Xiao, X.M., Wang, Y.F., Jiang, S., Ojima, D.S. and Bonham, C.D. (1995) Interannual Varia-tion in the Climate and Aboveground Biomass of Leymus-Chinense Steppe and Stipa-Grandis Steppe in the Xilin River Basin, Inner-Mongolia, China. J. Arid Environ. 31, 283–299.CrossRefGoogle Scholar
  66. Xiao, X.M., Zhang, Q.Y., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., Moore, B. and Ojima, D. (2004b) Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens. Environ. 91, 256–270.CrossRefGoogle Scholar
  67. Xiao, X.M., Zhang, Q.Y., Saleska, S., Hutyra, L., De Camargo, P., Wofsy, S., Frolking, S., Boles, S., Keller, M. and Moore, B. (2005) Satellite-based modeling of gross primary pro-duction in a seasonally moist tropical evergreen forest. Remote Sens. Environ. 94, 105–22.CrossRefGoogle Scholar
  68. Zhang, Q.Y., Xiao, X.M., Braswell, B., Linder, E., Baret, F. and Moore, B. (2005) Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sens. Environ. 99, 357–371.CrossRefGoogle Scholar
  69. Zhang, X.Y., Friedl, M.A. and Schaaf, C.B. (2006) Global vegetation phenology from Moder-ate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res. 111, G04017.CrossRefGoogle Scholar
  70. Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C. and Huete, A. (2003) Monitoring vegetation phenology using MODIS. Remote Sens. En-viron. 84, 471–475.CrossRefGoogle Scholar
  71. Zhao, F.H., Yu, G.R., Li, S.G., Ren, C.Y., Sun, X.M., Mi, N., Li, J. and Ouyang, Z. (2007) Canopy water use efficiency of winter wheat in the North China Plain. Agric. Water Manage. 93, 99–108.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Xiangming Xiao
    • 1
  • Junhui Zhang
    • 2
  • Huimin Yan
    • 3
  • Weixing Wu
    • 3
  • Chandrashekhar Biradar
    • 1
  1. 1.Department of Botany and Microbiology, and Center for Spatial AnalysisUniversity of OklahomaNormanUSA
  2. 2.Institute of EcologyChinese Academy of SciencesBeijingChina
  3. 3.Institute of Geographic Science and Natural Resources ResearchChinese Academy of SciencesBeijingChina

Personalised recommendations