Indices of Vegetation Activity

  • Alfredo Huete
  • Tomoaki Miura
  • Hiroki Yoshioka
  • Piyachat Ratana
  • Mark Broich
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)


In this chapter we explain satellite-based vegetation indices (VIs) as dynamic spectral measures of vegetation activity. VIs are among the most widely used satellite products in monitoring ecosystems and agriculture, resource management, and estimations of many biophysical canopy properties. A theoretical basis for their formulation is presented and we describe how VIs are processed and composited from satellite imagery. Recent trends in their validation and quality assessment using in situ tower measurements are also discussed. Finally, a cross section of major findings involving the use of satellite VIs in ecological and climate science is presented and we conclude with research challenges and environmental issues that will drive future uses of satellite VIs.


Normalize Difference Vegetation Index Photosynthetically Active Radiation Leaf Area Index Gross Primary Productivity Land Surface Temperature 
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 work was partly carried out under a NOAA Cooperative Agreement, CICS-NC (NESDIS-NESDISPO-2009-2002050), and NASA NPP grant NNX11AH25G (Miura, P.I). The authors are very grateful for the review and challenging comments provided by Richard Waring.


  1. Anderson LO (2012) Biome-scale forest properties in Amazonia based on field and satellite observations. Remote Sens Basel 4(5):1245–1271. doi: 10.3390/Rs4051245 Google Scholar
  2. Anderson LO, Aragao LEOC, Shimabukuro YE, Almeida S, Huete A (2011) Fraction images for monitoring intra-annual phenology of different vegetation physiognomies in Amazonia. Int J Remote Sens 32(2):387–408. doi: 10.1080/01431160903474921 Google Scholar
  3. Anyamba A, Tucker CJ (2005) Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. J Arid Environ 63(3):596–614Google Scholar
  4. Asner GP (2009) Tropical forest carbon assessment: integrating satellite and airborne mapping approaches. Environ Res Lett 4(3):1748–9326Google Scholar
  5. Asner GP, Martin RE, Knapp DE, Tupayachi R, Anderson C, Carranza L, Martinez P, Houcheime M, Sinca F, Weiss P (2011) Spectroscopy of canopy chemicals in humid tropical forests. Remote Sens Environ 115(12):3587–3598Google Scholar
  6. Baccini A, Laporte N, Goetz SJ, Sun M, Dong H (2008) A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environ Res Lett 3(4):045011Google Scholar
  7. Baret F, Guyot G (1991) Potentials and limits of vegetation indexes for LAI and APAR assessment. Remote Sens Environ 35(2–3):161–173Google Scholar
  8. Bates LM, Hall AE (1981) Stomatal closure with soil-water depletion not associated with changes in bulk leaf water status. Oecologia 50(1):62–65. doi: 10.1007/Bf00378794 Google Scholar
  9. Beck PSA, Atzberger C, Hogda KA, Johansen B, Skidmore AK (2006) Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens Environ 100(3):321–334Google Scholar
  10. Brando PM, Goetz SJ, Baccini A, Nepstad DC, Beck PSA, Christman MC (2010) Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc Natl Acad Sci USA 107(33):14685–14690Google Scholar
  11. Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW (2005) Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci USA 102(42):15144–15148. doi: 10.1073/Pnas.0505734102 Google Scholar
  12. Broich M, Hansen M, Stolle F, Potapov P, Margono BA, Adusei B (2011a) Remotely sensed forest cover loss shows high spatial and temporal variation across Sumatera and Kalimantan, Indonesia 2000–2008. Environ Res Lett 6(1). doi: 10.1088/1748-9326/6/1/014010
  13. Broich M, Hansen MC, Potapov P, Adusei B, Lindquist E, Stehman SV (2011b) Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia. Int J Appl Earth Obs 13(2):277–291. doi: 10.1016/J.Jag.2010.11.004
  14. Caccamo G, Chisholm LA, Bradstock RA, Puotinen ML (2011) Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sens Environ 115(10):2626–2639Google Scholar
  15. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252Google Scholar
  16. Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88(4):677–684Google Scholar
  17. Ceccato P, Flasse S, Gregoire JM (2002a) Designing a spectral index to estimate vegetation water content from remote sensing data. Part 2. Validation and applications. Remote Sens Environ 82(2–3):198–207Google Scholar
  18. Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S (2002b) Designing a spectral index to estimate vegetation water content from remote sensing data. Part 1. Theoretical approach. Remote Sens Environ 82(2–3):188–197Google Scholar
  19. Chen X, Vierling L, Deering D, Conley A (2005) Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study. Int J Remote Sens 26(24):5433–5451Google Scholar
  20. Chuvieco E, Ventura G, Martin MP, Gomez I (2005) Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping. Remote Sens Environ 94(4):450–462Google Scholar
  21. Cihlar J, Ly H, Li ZQ, Chen J, Pokrant H, Huang FT (1997) Multitemporal, multichannel AVHRR data sets for land biosphere studies—artifacts and corrections. Remote Sens Environ 60(1):35–57Google Scholar
  22. Cohen WB, Maiersperger TK, Yang ZQ, Gower ST, Turner DP, Ritts WD, Berterretche M, Running SW (2003) Comparisons of land cover and LAI estimates derived from ETM plus and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sens Environ 88(3):233–255Google Scholar
  23. Crist EP, Cicone RC (1984) A physically-based transformation of thematic mapper data—the TM tasseled cap. IEEE Trans Geosci Remote Sens 22(3):256–263Google Scholar
  24. Fassnacht KS, Gower ST, MacKenzie MD, Nordheim EV, Lillesand TM (1997) Estimating the leaf area index of North Central Wisconsin forests using the Landsat Thematic Mapper. Remote Sens Environ 61(2):229–245Google Scholar
  25. Fensholt R (2004) Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of terra MODIS and NOAA AVHRR satellite data. Int J Remote Sens 25(9):1641–1659Google Scholar
  26. Fensholt R, Sandholt I, Rasmussen MS (2004) Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens Environ 91(3–4):490–507Google Scholar
  27. Fensholt R, Sandholt I, Stisen S, Tucker C (2006) Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor. Remote Sens Environ 101(2):212–229Google Scholar
  28. Field CB, Randerson JT, Malmstrom CM (1995) Global net primary production—combining ecology and remote-sensing. Remote Sens Environ 51(1):74–88Google Scholar
  29. Fischer A (1994) A model for the seasonal-variations of vegetation indexes in coarse resolution data and its inversion to extract crop parameters. Remote Sens Environ 48(2):220–230Google Scholar
  30. Franklin KA, Lyons K, Nagler PL, Lampkin D, Glenn EP, Molina-Freaner F, Markow T, Huete AR (2006) Buffelgrass (Pennisetum ciliare) land conversion and productivity in the plains of Sonora, Mexico. Biol Conserv 127(1):62–71Google Scholar
  31. Gallo KP, Eidenshink JC (1988) Differences in visible and near-IR responses, and derived vegetation indexes, for the NOAA-9 and NOAA-10 AVHRRS—a case-study. Photogramm Eng Remote Sens 54(4):485–490Google Scholar
  32. Gallo K, Li L, Reed B, Eidenshink J, Dwyer J (2005) Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data. Remote Sens Environ 99(3):221–231Google Scholar
  33. Gamon JA, Penuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41(1):35–44Google Scholar
  34. Ganguly S, Friedl MA, Tan B, Zhang XY, Verma M (2010) Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product. Remote Sens Environ 114(8):1805–1816Google Scholar
  35. Gao BC (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266Google Scholar
  36. Gao X, Huete AR, Ni WG, Miura T (2000) Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sens Environ 74(3):609–620Google Scholar
  37. Gao F, Masek J, Schwaller M, Hall F (2006) On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans Geosci Remote Sens 44(8):2207–2218Google Scholar
  38. Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011) Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecol Biogeogr 20(1):154–159Google Scholar
  39. Gitelson AA, Kaufman YJ (1998) MODIS NDVI optimization to fit the AVHRR data series spectral considerations. Remote Sens Environ 66(3):343–350Google Scholar
  40. Gitelson AA, Gritz Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol 160(3):271–282Google Scholar
  41. Glenn EP, Huete AR, Nagler PL, Hirschboeck KK, Brown P (2007) Integrating remote sensing and ground methods to estimate evapotranspiration. Crit Rev Plant Sci 26(3):139–168Google Scholar
  42. Glenn EP, Huete AR, Nagler PL, Nelson SG (2008) Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors Basel 8(4):2136–2160Google Scholar
  43. Glenn EP, Doody TM, Guerschman JP, Huete AR, King EA, McVicar TR, Van Dijk AIJM, Van Niel TG, Yebra M, Zhang YQ (2011) Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience. Hydrol Process 25(26):4103–4116. doi: 10.1002/Hyp.8391 Google Scholar
  44. Gobron N, Pinty B, Verstraete MM, Widlowski JL (2000) Advanced vegetation indices optimized for up-coming sensors: design, performance, and applications. IEEE Trans Geosci Remote Sens 38(6):2489–2505Google Scholar
  45. Graetz RD (1990) Remote sensing of terrestrial ecosystem structure: an ecologist’s pragmatic view. In: Hobbs RJ, Mooney HA (eds) Remote sensing of biosphere functioning. Springer, New YorkGoogle Scholar
  46. Guerschman JP, Van Dijk AIJM, Mattersdorf G, Beringer J, Hutley LB, Leuning R, Pipunic RC, Sherman BS (2009) Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. J Hydrol 369(1–2):107–119. doi: 10.1016/J.Jhydrol.2009.02.013 Google Scholar
  47. Hilker T, Coops NC, Schwalm CR, Jassal RS, Black TA, Krishnan P (2008) Effects of mutual shading of tree crowns on prediction of photosynthetic light-use efficiency in a coastal Douglas-fir forest. Tree Physiol 28(6):825–834Google Scholar
  48. Hilker T, Gitelson A, Coops NC, Hall FG, Black TA (2011) Tracking plant physiological properties from multi-angular tower-based remote sensing. Oecologia 165(4):865–876Google Scholar
  49. Holben BN (1986) Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens 7(11):1417–1434Google Scholar
  50. Holben BN, Eck TF, Slutsker I, Tanre D, Buis JP, Setzer A, Vermote E, Reagan JA, Kaufman YJ, Nakajima T, Lavenu F, Jankowiak I, Smirnov A (1998) AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens Environ 66(1):1–16Google Scholar
  51. Houborg RM, Soegaard H (2004) Regional simulation of ecosystem CO2 and water vapor exchange for agricultural land using NOAA AVHRR and Terra MODIS satellite data. Application to Zealand, Denmark. Remote Sens Environ 93(1–2):150–167Google Scholar
  52. Huemmrich KF, Privette JL, Mukelabai M, Myneni RB, Knyazikhin Y (2005) Time-series validation of MODIS land biophysical products in a Kalahari woodland, Africa. Int J Remote Sens 26(19):4381–4398Google Scholar
  53. Huete A (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3): 295–309Google Scholar
  54. Huete AR, Glenn EP (2011) Recent advances in remote sensing of ecosystem structure and function. In: Weng Q (ed) Advances in environmental remote sensing: sensors, algorithms, and applications. CRC Press/Taylor and Francis Group, New YorkGoogle Scholar
  55. Huete A, Didan, K, van Leeuwen W, Miura T, Glenn E (2011) MODIS Vegetation Indices, In: Ramachandran B, Justice CO, Abrams M (eds) Land remote sensing and global environmental change: NASA’s earth observing system and the science of ASTER and MODIS, vol 11. Springer, BerlinGoogle Scholar
  56. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213Google Scholar
  57. Huete AR, Didan K, Shimabukuro YE, Ratana P, Saleska SR, Hutyra LR, Yang WZ, Nemani RR, Myneni R (2006) Amazon rainforests green-up with sunlight in dry season. Geophys Res Lett 33(6):L06405Google Scholar
  58. Huete A, Keita F, Thome K, Privette J, van Leeuwen WJD, Justice C, Morisette J (1999) A light aircraft radiometric package for MODLAND quick airborne looks (MQUALS). The Earth Observer 11(1):22Google Scholar
  59. Huete AR, Restrepo-Coupe N, Ratana P, Didan K, Saleska SR, Ichii K, Panuthai S, Gamo M (2008) Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia. Agric For Meteorol 148(5):748–760Google Scholar
  60. Hunt ER, Rock BN (1989) Detection of changes in leaf water-content using near-infrared and middle-infrared reflectances. Remote Sens Environ 30(1):43–54Google Scholar
  61. Ichii K, Hashimoto H, White MA, Potter C, Hutyra LR, Huete AR, Myneni RB, Nemanis RR (2007) Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality. Global Change Biol 13(1):67–77Google Scholar
  62. IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  63. Jahan N, Gan TY (2009) Modeling gross primary production of deciduous forest using remotely sensed radiation and ecosystem variables. J Geophys Res Biogeosci 114Google Scholar
  64. Jiang ZY, Huete AR, Chen J, Chen YH, Li J, Yan GJ, Zhang XY (2006a) Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens Environ 101(3):366–378Google Scholar
  65. Jiang ZY, Huete AR, Li J, Chen YH (2006b) An analysis of angle-based with ratio-based vegetation indices. IEEE Trans Geosci Remote Sens 44(9):2506–2513Google Scholar
  66. Jiang Z, Huete A, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ 112(10):3833–3845Google Scholar
  67. Jin SM, Sader SA (2005) MODIS time-series imagery for forest disturbance detection and quantification of patch size effects. Remote Sens Environ 99(4):462–470Google Scholar
  68. Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Knyazikhin Y, Running SW, Nemani RR, Wan ZM, Huete AR, van Leeuwen W, Wolfe RE, Giglio L, Muller JP, Lewis P, Barnsley MJ (1998) The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36(4):1228–1249Google Scholar
  69. Kaufman YJ, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote Sens 30(2):261–270Google Scholar
  70. Kaufman YJ, Remer LA, Tanre D, Li RR, Kleidman R, Mattoo S, Levy RC, Eck TF, Holben BN, Ichoku C, Martins JV, Koren I (2005) A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean. IEEE Trans Geosci Remote Sens 43(12):2886–2897Google Scholar
  71. Kawamura K, Akiyama T, Yokota H, Tsutsumi M, Yasuda T, Watanabe O, Wang G, Wang S (2005) Monitoring of forage conditions with MODIS imagery in the Xilingol steppe, Inner Mongolia. Int J Remote Sens 26(7):1423–1436Google Scholar
  72. Kerr JT, Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends Ecol Evol 18(6):299–305Google Scholar
  73. Kobayashi H, Dye DG (2005) Atmospheric conditions for monitoring the long-terrn vegetation dynamics in the Amazon using normalized difference vegetation index. Remote Sens Environ 97(4):519–525Google Scholar
  74. Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) Lidar remote sensing for ecosystem studies. Bioscience 52(1):19–30Google Scholar
  75. Li FQ, Kustas WP, Anderson MC, Prueger JH, Scott RL (2008) Effect of remote sensing spatial resolution on interpreting tower-based flux observations. Remote Sens Environ 112(2):337–349. doi:  10.1016/J.Rse.2006.11.032 Google Scholar
  76. Lobser SE, Cohen WB (2007) MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data. Int J Remote Sens 28(22):5079–5101Google Scholar
  77. Los SO (1993) Calibration adjustment of the NOAA-AVHRR normalized difference vegetation index without recourse to component channel 1 and 2 data. Int J Remote Sens 14(10):1907–1917Google Scholar
  78. Mahadevan P, Wofsy SC, Matross DM, Xiao XM, Dunn AL, Lin JC, Gerbig C, Munger JW, Chow VY, Gottlieb EW (2008) A satellite-based biosphere parameterization for net ecosystem CO2 exchange: vegetation photosynthesis and respiration model (VPRM). Global Biogeochem Cycle 22(2). doi: 10.1029/2006gb002735
  79. Middleton EM, Huemmrich KF, Cheng YB, Margolis HA (2011) Spectral bio-indicators of photosynthetic efficiency and vegetation stress. In: Thenkabail PS, Lyon JG, Huete AR (eds) Hyperspectral remote sensing of vegetation. Taylor & Francis Group, LondonGoogle Scholar
  80. Mildrexler DJ, Zhao MS, Running SW (2009) Testing a MODIS global disturbance index across North America. Remote Sens Environ 113(10):2103–2117Google Scholar
  81. Miura T, Huete AR, Yoshioka H (2000) Evaluation of sensor calibration uncertainties on vegetation indices for MODIS. IEEE Trans Geosci Remote Sens 38(3):1399–1409Google Scholar
  82. Miura T, Huete AR, Yoshioka H, Holben BN (2001) An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sens Environ 78(3):284–298Google Scholar
  83. Miura T, Huete A, Yoshioka H (2006) An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 hyperion data. Remote Sens Environ 100(2):223–236Google Scholar
  84. Monteith JL, Unsworth MH (1990) Principles of environmental physics, 2nd edn. Edward Arnold, LondonGoogle Scholar
  85. Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386(6626):698–702Google Scholar
  86. Nagler PL, Cleverly J, Glenn E, Lampkin D, Huete A, Wan ZM (2005a) Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data. Remote Sens Environ 94(1):17–30Google Scholar
  87. Nagler PL, Scott RL, Westenburg C, Cleverly JR, Glenn EP, Huete AR (2005b) Evapotranspiration on western US rivers estimated using the enhanced vegetation index from MODIS and data from eddy covariance and Bowen ratio flux towers. Remote Sens Environ 97(3):337–351Google Scholar
  88. Nagler PL, Glenn EP, Kim H, Emmerich W, Scott RL, Huxman TE, Huete AR (2007) Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices. J Arid Environ 70(3):443–462Google Scholar
  89. Penuelas J, Rutishauser T, Filella I (2009) Phenology feedbacks on climate change. Science 324(5929):887–888Google Scholar
  90. Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20(9):503–510Google Scholar
  91. Potapov PV, Turubanova SA, Hansen MC, Adusei B, Broich M, Altstatt A, Mane L, Justice CO (2012) Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+data. Remote Sens Environ 122:106–116. doi: 10.1016/j.rse.2011.08.027 Google Scholar
  92. Potter C, Klooster S, Huete A, Genovese V (2007) Terrestrial carbon sinks for the United States predicted from MODIS satellite data and ecosystem modeling. Earth Interact 11. doi: 10.1175/Ei228.1
  93. Privette JL, Asner GP, Conel J, Huemmrich KF, Olson R, Rango A, Rahman AF, Thome K, Walter-Shea EA (2000) The EOS prototype validation exercise (PROVE) at Jornada: overview and lessons learned. Remote Sens Environ 74(1):1–12Google Scholar
  94. Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126Google Scholar
  95. Rahman AF, Sims DA, Cordova VD, El-Masri BZ (2005) Potential of MODIS EVI and surface temperature for directly estimating per-pixel ecosystem C fluxes. Geophys Res Lett 32(19):L19404Google Scholar
  96. Ratana P, Huete AR, Ferreira L (2005) Analysis of cerrado physiognomies and conversion in the MODIS seasonal-temporal domain. Earth Interact 9:1–22Google Scholar
  97. Reed BC, White M, Brown JF (2003) Remote sensing phenology. In: Schwartz MD (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, The NetherlandsGoogle Scholar
  98. Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43(12):1541–1552Google Scholar
  99. Richardson AD, Jenkins JP, Braswell BH, Hollinger DY, Ollinger SV, Smith ML (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152(2):323–334Google Scholar
  100. Ripullone F, Rivelli AR, Baraldi R, Guarini R, Guerrieri R, Magnani F, Penuelas J, Raddi S, Borghetti M (2011) Effectiveness of the photochemical reflectance index to track photosynthetic activity over a range of forest tree species and plant water statuses. Funct Plant Biol 38(3):177–186Google Scholar
  101. Rocha AV, Shaver GR (2009) Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agric For Meteorol 149(9):1560–1563Google Scholar
  102. Roderick M, Smith R, Cridland S (1996) The precision of the NDVI derived from AVHRR observations. Remote Sens Environ 56(1):57–65Google Scholar
  103. Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: 3rd ERTS symposium, NASA SP-351, pp 309–317Google Scholar
  104. Roy DP, Ju J, Lewis P, Schaaf C, Gao F, Hansen M, Lindquist E (2008) Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens Environ 112(6):3112–3130. doi: 10.1016/J.Rse.2008.03.009
  105. Roy DP, Ju JC, Kline K, Scaramuzza PL, Kovalskyy V, Hansen M, Loveland TR, Vermote E, Zhang CS (2010) Web-enabled Landsat Data (WELD): Landsat ETM plus composited mosaics of the conterminous United States. Remote Sens Environ 114(1):35–49Google Scholar
  106. Running SW, Baldocchi DD, Turner DP, Gower ST, Bakwin PS, Hibbard KA (1999) A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens Environ 70(1):108–127Google Scholar
  107. Running SW, Nemani RR, Heinsch FA, Zhao MS, Reeves M, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54(6):547–560Google Scholar
  108. Ryu Y, Baldocchi DD, Verfaillie J, Ma S, Falk M, Ruiz-Mercado I, Hehn T, Sonnentag O (2010) Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function. Agr For Meteorol 150(12):1597–1606Google Scholar
  109. Ryu Y, Baldocchi DD, Kobayashi H, van Ingen C, Li J, Black TA, Beringer J, van Gorsel E, Knohl A, Law BE, Roupsard O (2011) Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Global Biogeochem Cycle 25Google Scholar
  110. Sakamoto T, Van Nguyen N, Ohno H, Ishitsuka N, Yokozawa M (2006) Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens Environ 100(1):1–16Google Scholar
  111. Saleska SR, Didan K, Huete AR, da Rocha HR (2007) Amazon forests green-up during 2005 drought. Science 318(5850):612Google Scholar
  112. Samanta A, Ganguly S, Hashimoto H, Devadiga S, Vermote E, Knyazikhin Y, Nemani RR, Myneni RB (2010) Amazon forests did not green-up during the 2005 drought. Geophys Res Lett 37:L05401Google Scholar
  113. Schaaf CB, Gao F, Strahler AH, Lucht W, Li XW, Tsang T, Strugnell NC, Zhang XY, Jin YF, Muller JP, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, d’Entremont RP, Hu BX, Liang SL, Privette JL, Roy D (2002) First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens Environ 83(1–2):135–148Google Scholar
  114. Schmidt M, Udelhoven T, Gill T, Roder A (2012) Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna. J Appl Remote Sens 6. doi: 10.1117/1.Jrs.6.063512
  115. Schubert P, Eklundh L, Lund M, Nilsson M (2010) Estimating northern peatland CO2 exchange from MODIS time series data. Remote Sens Environ 114(6):1178–1189Google Scholar
  116. Schwartz MD, Hanes JM (2010) Continental-scale phenology: warming and chilling. Int J Climatol 30(11):1595–1598Google Scholar
  117. Sellers PJ (1985) Canopy reflectance, photosynthesis and transpiration. Int J Remote Sens 6(8):1335–1372Google Scholar
  118. Sims DA, Rahman AF, Cordova VD, El-Masri BZ, Baldocchi DD, Flanagan LB, Goldstein AH, Hollinger DY, Misson L, Monson RK, Oechel WC, Schmid HP, Wofsy SC, Xu LK (2006) On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. J Geophys Res Biogeosci 111(G4)Google Scholar
  119. Sims DA, Rahman AF, Cordova VD, El-Masri BZ, Baldocchi DD, Bolstad PV, Flanagan LB, Goldstein AH, Hollinger DY, Misson L, Monson RK, Oechel WC, Schmid HP, Wofsy SC, Xu L (2008) A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote Sens Environ 112(4):1633–1646Google Scholar
  120. Smith AMS, Falkowski MJ, Hudak AT, Evans JS, Robinson AP, Steele CM (2009) A cross-comparison of field, spectral, and lidar estimates of forest canopy cover. Can J Remote Sens 35(5):447–459Google Scholar
  121. Soudani K, Hmimina G, Delpierre N, Pontailler JY, Aubinet M, Bonal D, Caquet B, de Grandcourt A, Burban B, Flechard C, Guyon D, Granier A, Gross P, Heinesh B, Longdoz B, Loustau D, Moureaux C, Ourcival JM, Rambal S, Saint André L, Dufrêne E (2012) Ground-based network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sens Environ 123(0):234–245. doi: 10.1016/j.rse.2012.03.012
  122. Souza C, Firestone L, Silva LM, Roberts D (2003) Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models. Remote Sens Environ 87(4):494–506Google Scholar
  123. Trishchenko AP, Cihlar J, Li Z (2002) Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sens Environ 81(1):1–18Google Scholar
  124. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150Google Scholar
  125. Tucker CJ, Grant DM, Dykstra JD (2004) NASA’s global orthorectified landsat data set. Photogramm Eng Remote Sens 70(3):313–322Google Scholar
  126. Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, El Saleous N (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26(20):4485–4498Google Scholar
  127. Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing for biodiversity science and conservation. Trends Ecol Evol 18(6):306–314Google Scholar
  128. Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54(6):523–534Google Scholar
  129. van Leeuwen WJD, Huete AR, Laing TW (1999) MODIS vegetation index compositing approach: a prototype with AVHRR data. Remote Sens Environ 69(3):264–280Google Scholar
  130. van Leeuwen WJD, Orr BJ, Marsh SE, Herrmann SM (2006) Multi-sensor NDVI data continuity: uncertainties and implications for vegetation monitoring applications. Remote Sens Environ 100(1):67–81Google Scholar
  131. Verstraete MM, Pinty B (1996) Designing optimal spectral indexes for remote sensing applications. IEEE Trans Geosci Remote Sens 34(5):1254–1265Google Scholar
  132. Vickers D, Thomas CK, Pettijohn C, Martin JG, Law BE (2012) Five years of carbon fluxes and inherent water-use efficiency at two semi-arid pine forests with different disturbance histories. Tellus B 64. doi: 10.3402/Tellusb.V64i0.17159
  133. Vuolo F, Dash J, Curran PJ, Lajas D, Kwiatkowska E (2012) Methodologies and uncertainties in the use of the terrestrial chlorophyll index for the Sentinel-3 mission. Remote Sens Basel 4(5):1112–1133Google Scholar
  134. 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(1–2):225–237Google Scholar
  135. Wang Q, Adiku S, Tenhunen J, Granier A (2005) On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens Environ 94(2):244–255Google Scholar
  136. Wang YJ, Lyapustin AI, Privette JL, Morisette JT, Holben B (2009) Atmospheric correction at AERONET locations: a new science and validation data set. IEEE Trans Geosci Remote Sens 47(8):2450–2466Google Scholar
  137. Waring RH, Whitehead D, Jarvis PG (1979) The contribution of stored water to transpiration in Scots pine. Plant Cell Environ 2(4). doi: 10.1111/j.1365-3040.1979.tb00085.x
  138. Waring RH, Coops NC, Fan W, Nightingale JM (2006) MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous USA. Remote Sens Environ 103(2):218–226Google Scholar
  139. Wilson TB, Meyers TP (2007) Determining vegetation indices from solar and photosynthetically active radiation fluxes. Agric For Meteorol 144(3–4):160–179Google Scholar
  140. Wolfe RE, Roy DP, Vermote E (1998) MODIS land data storage, gridding, and compositing methodology: level 2 grid. IEEE Trans Geosci Remote Sens 36(4):1324–1338Google Scholar
  141. Xiao XM, Braswell B, Zhang QY, Boles S, Frolking S, Moore B (2003) Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia. Remote Sens Environ 84(3):385–392Google Scholar
  142. Xiao XM, Zhang QY, Braswell B, Urbanski S, Boles S, Wofsy S, Berrien M, Ojima D (2004) Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens Environ 91(2):256–270Google Scholar
  143. Xiao XM, Zhang QY, Saleska S, Hutyra L, De Camargo P, Wofsy S, Frolking S, Boles S, Keller M, Moore B (2005) Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest. Remote Sens Environ 94(1):105–122Google Scholar
  144. Xiao XM, Boles S, Frolking S, Li CS, Babu JY, Salas W, Moore B (2006) Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ 100(1):95–113Google Scholar
  145. Xu LA, Samanta A, Costa MH, Ganguly S, Nemani RR, Myneni RB (2011) Widespread decline in greenness of Amazonian vegetation due to the 2010 drought. Geophys Res Lett 38. doi: 10.1029/2011gl046824
  146. Yamamoto H, Matsumura Y, Sawayama S (2005) Evaluation of supply potential of energy crops in Japan considering cases of improvement of crop productivity. Biomass Bioenerg 29(5):355–359Google Scholar
  147. Yang FH, White MA, Michaelis AR, Ichii K, Hashimoto H, Votava P, Zhu AX, Nemani RR (2006) Prediction of continental-scale evapotranspiration by combining MODIS and AmeriFlux data through support vector machine. IEEE Trans Geosci Remote Sens 44(11):3452–3461Google Scholar
  148. Yang X, Mustard JF, Tang J, Xu H (2012) Regional-scale phenology modeling based on meteorological records and remote sensing observations. J Geophys Res 117(G3):1–18. doi: 10.1029/2012JG001977 Google Scholar
  149. Yoshioka H, Miura T, Obata K (2012) Derivation of relationships between spectral vegetation indices from multiple sensors based on vegetation isolines. Remote Sens 4(3):583–597. doi: 10.3390/rs4030583 Google Scholar
  150. Yoshioka H, Huete AR, Miura T (2000) Derivation of vegetation isoline equations in red-NIR reflectance space. IEEE Trans Geosci Remote Sens 38(2):838–848Google Scholar
  151. Zarco-Tejada PJ, Rueda CA, Ustin SL (2003) Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens Environ 85(1):109–124Google Scholar
  152. Zha Y, Gao J, Ni S, Shen N (2005) Temporal filtering of successive MODIS data in monitoring a locust outbreak. Int J Remote Sens 26(24):5665–5674Google Scholar
  153. Zhang XY, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84(3):471–475Google Scholar
  154. Zhang XY, Friedl MA, Schaaf CB, Strahler AH, Schneider A (2004) The footprint of urban climates on vegetation phenology. Geophys Res Lett 31(12)Google Scholar
  155. Zhang QY, Xiao XM, Braswell B, Linder E, Baret F, 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(3):357–371Google Scholar
  156. Zhang XY, Friedl MA, Schaaf CB (2006) Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J Geophys Res Biogeosci 111(G4)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alfredo Huete
    • 1
  • Tomoaki Miura
    • 2
  • Hiroki Yoshioka
    • 3
  • Piyachat Ratana
    • 1
  • Mark Broich
    • 1
  1. 1.University of Technology SydneyNSWAustralia
  2. 2.University of HawaiiHonoluluUSA
  3. 3.Aichi Prefectural UniversityAichiJapan

Personalised recommendations