Theoretical and Applied Climatology

, Volume 98, Issue 1–2, pp 171–186 | Cite as

Comparison of satellite- and ground-based NDVI above different land-use types

  • A. Tittebrand
  • U. Spank
  • CH. Bernhofer
Original Paper


In order to evaluate the use of satellite (moderate resolution imaging spectroradiometer: MODIS) and ground-measured (hyperspectral spectrometer and broadband micrometeorological sensors) normalized difference vegetation index (NDVI), this study compares NDVI derived from five experimental (FLUXNET) field sites (grassland, winter wheat, corn, spruce, and beech) in Germany in June 2006 and April-September 2007. In addition, the spatial variability of ground radiation measured within one specific land-use class (for grass and winter wheat) was investigated to analyze the accuracy of the FLUXNET tower values. Furthermore, the angular dependence of spectrometer values on viewing angles was determined in order to enhance the spatial representativeness of spectrometer measurements which, especially above trees, are affected by soil parts and the tower structure when measured in nadir. The best agreement between the satellite- and ground-measured NDVI was found for winter wheat (2006) with values from 0.79–0.88 followed by grass (2006), showing NDVI values between 0.71 and 0.86. The spatial variability of NDVI within one land-use type was lower than the differences caused by the different NDVI determination methods. Above more open canopies (corn, beech), spectrometer measurements with 60° viewing angle in solar plane direction were found to better correspond to satellite-derived NDVI. Together with broadband NDVI, our ground-based results can complement satellite-derived NDVI.


Normalize Difference Vegetation Index Winter Wheat Photosynthetic Active Radiation Spectrometer Measurement Solar Elevation Angle 
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.



This study was founded by CarboEurope IP, the Deutsche Forschungsgemeinschaft (DFG), and the DFG-project MAGIM (Matter Fluxes in Grasslands of Inner Mongolia as Influenced by Stocking Rate). Special thanks to T. Grünwald for providing the tower data of the anchor stations and for helpful information and discussions and K. Geidel for the determination of the area of the individual land use parts around the tower stations.


  1. Baldocchi D, Falge E, Gu L, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer Ch, Davis K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee X, Malhi Y, Meyers T, Munger W, Oechel W, Paw UKT, Pilegaard K, Schmid HP, Valentini R, Verma S, Vesala T, Wilson K 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. B Am Meteorol Soc 82:2415–2434CrossRefGoogle Scholar
  2. Barbosa HA, Heute AR, Baethgen WE (2006) A 20-year study of NDVI variability over the northeast region of Brazil. J Arid Environ 67:288–307CrossRefGoogle Scholar
  3. Bernhofer C, Aubinet M, Clement R, Grelle A, Grünwald T, Ibrom A, Jarvis P, Rebmann C, Schulze E-D, Tenhunen JD (2003) Spruce forests (Norway and Sitka spruce, including Douglas fir): Carbon and water fluxes and balances, ecological and ecophysiological determinants. In: Biospheric Exchanges of Carbon, Water and Energy of European Forests, Ecological Studies, 163:99-124, Springer-Verlag, Berlin HeidelbergGoogle Scholar
  4. Cihlar J, Ly H, Li Z, Chen J, Pokrant H, Huang F (1997) Multitemporal, multichannel AVHRR data sets for land biosphere studies–artifacts and corrections. Remote Sens Environ 60:35–57CrossRefGoogle Scholar
  5. Disney M, Lewis P, Thackrah G, Quaife T, Barnsley M (2004) Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales. Int J Remote Sens 25(23):5297–5317CrossRefGoogle Scholar
  6. Falk M, Meyers T, Black A, Barr A, Yamamoto S, Verma S, Baldocchi D (2004) Seasonal course of normalized difference vegetation index ‘NDVI’ derived from tower data. 26th Conference on Agricultural and Forest Meteorology, Vancouver, British Columbia, 23-26. August 2004, Session 2.10Google Scholar
  7. Fang H, Liang Sh, McCharan MP, van Leeuwen WJD, Drake S, Marsh SE, Thomson AM, Izaurralde RC, Rosenberg NJ (2005) Biophysical characterisation and management effects on semiarid rangelands observed from Landsat ETM+ data. IEEE Geosci Remotes 43(1):125–134CrossRefGoogle Scholar
  8. Fluxnet (2007), access 16.6.2006, 21.01.2007, 30.8.2007
  9. Göckede M, Foken T, Aubinet M, Aurela M, Banza J, Bernhofer C, Bonnefond JM, Brunet Y, Carrara A, Clement R, Dellwik E, Elbers J, Eugster W, Fuhrer J, Granier A, Grünwald T, Heinesch B, Janssens IA, Knohl A, Koeble R, Laurila T, Longdoz B, Manca B, Marek M, Markkanen T, Mateus J, Matteucci G, Mauder M, Migliavacca M, Minerbi S, Moncrieff J, Montagnani L, Moors E, Ourcival J-M, Papale D, Pereira J, Pilegaard K, Pita G, Rambal S, Rebmann C, Rodrigues A, Rotenberg E, Sanz MJ, Sedlak P, Seufert G, Siebicke L, Soussana JF, Valentini R, Vesala T, Verbeeck H, Yakir D (2008) Quality control of CarboEurope flux data–Part 1: Coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences 5:433–450CrossRefGoogle Scholar
  10. Grünwald T, Bernhofer C (2007) A decade of carbon, water and energy flux measurements of an old spruce forest at the Anchor Station Tharandt. TELLUS, 59B, pp 387–396. Blackwell Publishing, OxfordGoogle Scholar
  11. Hill MJ, Held AA, Leuning R, Coops NC, Hughes D, Cleugh HA (2006) MODIS spectral signals at a flux tower site: relationships with high-resolution data, and CO2 flux and light use efficiency measurements. Remote Sens Environ 103:351–368CrossRefGoogle Scholar
  12. Huemmrich KF, Black TA, Jarvis PG, McCaughey JH, Hall FG (1999) High temporal resolution NDVI phenology from micrometeorological radiation sensors. J Geophys Res 104(22):27935–27944CrossRefGoogle Scholar
  13. Huete AR, Liu HQ (1994) An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the NDVI for the MODIS-EOS. IEEE Geosci Remote 32(4):897–905CrossRefGoogle Scholar
  14. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferriera LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 82:195–213CrossRefGoogle Scholar
  15. Jenkins JP, Richardson AD, Braswell BH, Ollinger SV, Hollinger DY, Smith M-L (2007) Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agr Forest Meteorol 143:64–79CrossRefGoogle Scholar
  16. Jiang Z, Huete AR, Chen J, Chen Y, Li J, Yan G, Zhang X (2006) Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens Environ 101:366–378CrossRefGoogle Scholar
  17. Kumar L, Schmidt KS, Dury S, Skimore AK (2001) Imaging spectrometry and vegetation science. In: Van der Meer F, De Jong SM (eds) Imaging spectrometry. Kluwer, Dordrecht, pp 111–155Google Scholar
  18. Leprieur C, Kerr YH, Mastorchio S, Meunier JC (2000) Monitoring vegetation cover across semi-arid regions: comparison of remote observations from various scales. Int J Remote Sens 21, 2:281–300Google Scholar
  19. Mellmann P, Grünwald T, Frühauf C, Podlasly C, Bernhofer C (2003) Eine objektive Methode zur Erstellung eines repräsentativen Bestandesparametersatzes mit Hilfe der Quellflächen-Analyse für die Ankestation Tharander Wald, in Flussbestimmung an komplexen Standorten, Tharandter KlimaprotokolleGoogle Scholar
  20. MODIS (2007a), downloads July 2006 and November 2007
  21. MODIS (2007b)∼imswww/pub/imswelcome1/index.html, downloads July 2006 and November 2007
  22. Mutanga O, Skidmore AK, Wieren S (2003) Discrimination tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry. Isprs J Photogramm Remote Sens 57(4):263–272CrossRefGoogle Scholar
  23. Piao S, Mohammat A, Fang J, Cai Q, Feng J (2006) NDVI-based increase in growth of temperate grasslands and its response to climate changes in China. Global Environ Change 16:340–348CrossRefGoogle Scholar
  24. Reichstein M, Running S, Tenhunen J, Aubinet M, Bernhofer C, Buchmann N, Granier A, Grünwald T, Joffre R, Knohl A, Kowalski A, Loustau D, Ourcival J-M, Pereira JS, Rambal S, Seufert G, Valentini R, Vesala T, Zhao M (2003a) MODIS land validation at European eddy covariance flux tower (FLUXNET) sites. IGARSS Meeting, ToulouseGoogle Scholar
  25. Reichstein M, Running S, Tenhunen J, Aubinet M, Bernhofer C, Buchmann N, Granier A, Grünwald T, Joffre R, Knohl A, Kowalski A, Loustau D, Ourcival J-M, Pereira JS, Rambal S, Seufert G, Valentini R, Vesala T, Zhao M (2003b) Evaluation of MODIS-driven estimates of vegetation productivity at European FLUXNET sites. EGS Trans Ann Meet Eur Geophys Soc, NiceGoogle Scholar
  26. Reichstein M, Papale D, Valentini R, Aubinet M, Bernhofer C, Knohl A, Laurila T, Lindroth A, Moors E, Pilegaard K, Seufert G (2007b) Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites. Geophys Res Lett 34:L01402. doi: 10.1029/2006GL027880 CrossRefGoogle Scholar
  27. Ross J, Sulev M (2000) Sources of errors in measurements of PAR. Agr Forest Meteorol 100:103–125CrossRefGoogle Scholar
  28. Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC (1974) Monitoring the vernal advancements and retrogradation of natural vegetation. NASA/GSFC. Final Report, Greenbelt, MD, USA, pp 1–137Google Scholar
  29. Van Leeuwen WJD, Huete AR, Laing TW (1999) MODIS vegetation index compositing approach: a prototype with AVHRR data. Remote Sens Environ 69:264–280CrossRefGoogle Scholar
  30. Van Til M, Bijlmer A, de Lange R (2004) Seasonal variability in spectral reflectance of coastal dune vegetation. EARSeL eProceedings 3, 2/2004Google Scholar
  31. Vermote EF, Saleus NZ El, Justice CO (2002) Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sens Environ 83:97–111CrossRefGoogle Scholar
  32. Wang Q, Tenhunen J, Dinh NQ, Reichstein M, Vesala T, Keronen P (2004) Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sens Environ 93:225–237CrossRefGoogle Scholar
  33. Wilson TB, Meyers TP (2007) Determining vegetation indices from solar and photosynthetically active radiation fluxes. Agr Forest Meteorol 144:160–179CrossRefGoogle Scholar
  34. Zhao D, Huang L, Li J, Qi J (2007) A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. Isprs J Photogramm Remote Sens 62:25–33CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Institute of Oceanography, Center for Marine and Atmospheric ScienceUniversity of HamburgHamburgGermany
  2. 2.Institute of Hydrology and MeteorologyTechnische Universität DresdenDresdenGermany

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