Green Leaf Area and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation

  • Sangram Ganguly
  • Ramakrishna R. Nemani
  • Frederic Baret
  • Jian Bi
  • Marie Weiss
  • Gong Zhang
  • Cristina Milesi
  • Hirofumi Hashimoto
  • Arindam Samanta
  • Aleixandre Verger
  • Kumaresh Singh
  • Ranga B. Myneni
Chapter
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)

Abstract

Leaf Area Index (LAI), the area of leaves per unit ground area, and the Fraction of Photosynthetically Active Radiation (FPAR; 400–700 nm) absorbed by vegetation are important biophysical variables for quantifying the cycling of water, carbon and nutrients through ecosystems. The LAI/FPAR products from the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and the Système Pour l’Observation de la Terre (SPOT) sensor have a large Earth science community user base and the ease of access, provision of pixel quality and validation information have greatly aided the use of these products. Recent research efforts focusing on inter-sensor product consistencies have developed a foundation upon which mature algorithms and a validation framework can act synergistically to further refine the accuracy and precision of these existing long-term products. This chapter provides a brief overview of the recent progresses in LAI/FPAR estimation algorithms and resulting biophysical products from the AVHRR, MODIS, SPOT and Landsat data.

Keywords

Normalize Difference Vegetation Index Leaf Area Index Advanced Very High Resolution Radiometer Advanced Very High Resolution Radiometer Vegetation Canopy 
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.

References

  1. Asrar G, Fuchs M, Kanemasu ET, Hatfield JL (1984) Estimating absorbed photosynthetic radiation and leaf-area index from spectral reflectance in wheat. Agron J 76(2):300–306CrossRefGoogle Scholar
  2. Baret F, Weiss M, Lacaze R, Camacho F, Makhmara H, Pacholcyzk P, Smets B (2013) GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: principles of development and production. Remote Sens Environ 137:299–309Google Scholar
  3. Baret F, Weiss M, Lacaze R, Camacho F, Pacholcyzk P, Smets B (2010) Consistent and accurate LAI, FAPAR, and FCover global products: principles and evaluation of GEOV1 products. In: Proceedings of 3rd RAQRS, 27th Sept.- 1st Oct., Torrent, pp 208–213Google Scholar
  4. Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B et al (2007) LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens Environ 110(3):275–286. http://www.sciencedirect.com/science/article/B6V6V-4NKJ1K0-1/2/29e421e7954752424d9bfbf9b697ca68
  5. Bonan GB, Levis S, Sitch S, Vertenstein M, Oleson KW (2003) A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics. Glob Change Biol 9(11):1543–1566. doi: 10.1046/j.1529-8817.2003.00681.x CrossRefGoogle Scholar
  6. Brown L, Chen JM, Leblanc SG, Cihlar J (2000) A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests an image and model analysis. Remote Sens Environ 71(1):16–25. doi: 10.1016/S0034-4257(99)00035-8 CrossRefGoogle Scholar
  7. Brown M, Pinzon JE, Didan K, Morisette JT, Tucker CJ (2006) Evaluation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM + sensors. IEEE Trans Geosci Remote Sens 44(7):1787–1793. doi: 10.1109/TGRS.2005.860205 CrossRefGoogle Scholar
  8. Camacho F, Baret F, Cernicharo J, Lacaze R, Weiss M (2012) Quality assessment of the first version of Geoland-2 biophysical variables produced at global scale. In: Sobrino J. (Ed.), Third international symposium on recent advances in quantitative remote sensing. TorrentGoogle Scholar
  9. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252CrossRefGoogle Scholar
  10. Chander G, Huang C, Yang L, Homer C, Larson C (2009) Developing consistent landsat data sets for large area applications: the mrlc 2001 protocol. IEEE Geosci Remote Sens Lett 6(4):777–781. doi: 10.1109/LGRS.2009.2025244 CrossRefGoogle Scholar
  11. Chen J (2002) Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sens Environ 80(1):165–184. doi: 10.1016/S0034-4257(01)00300-5 CrossRefGoogle Scholar
  12. Chen JM, Cihlar J (1996) Retrieving leaf area index of boreal conifer forests using landsat TM images. Remote Sens Environ 55(2):153–162CrossRefGoogle Scholar
  13. Chen JM, Rich PM, Gower ST, Norman JM, Plummer S (1997) Leaf area index of boreal forests: Theory, techniques, and measurements. J Geophys Res-Atmos 102(D24):29429–29443CrossRefGoogle Scholar
  14. Colombo R, Bellingeri D, Fasolini D, Marino CM (2003) Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sens Environ 86(1):120–131. doi: 10.1016/s0034-4257(03)00094-4 CrossRefGoogle Scholar
  15. Combal B, Baret F, Weiss M, Trubuil A, Mace D, Pragnere A, Myneni RB et al (2002) Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem. Remote Sens Environ 84(1):1–15. doi: 10.1016/S0034-4257(02)00035-4 CrossRefGoogle Scholar
  16. Demarty J, Chevallier F, Friend AD, Viovy N, Piao S, Ciais P (2007) Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model. Geophys Res Lett 34(15):L15402 doi: 10.1029/2007GL030014
  17. Devadiga S, Masuoka E, Brown M, Pinzon J, Tucker CJ, Roy DP, Ju J et al (2007) Generating a long-term land data record from the AVHRR and MODIS Instruments. In: IEEE geoscience and remote sensing symposium, 2007, IGARSS 2007. pp 1021–1025 doi: 10.1109/IGARSS.2007.4422974
  18. 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):229–245CrossRefGoogle Scholar
  19. Fernandes R, Butson C (2003) A Landsat TM/ETM + based accuracy assessment of leaf area index products for Canada derived from SPOT4/VGT data. Can J Remote Sens 29(2):241–258. http://scholar.google.com/scholar?q=A+Landsat+TM/ETM++based+accuracy+assessment+of+leaf+area+index+products+for+Canada+derived+from+SPOT4/VGT+data&hl=en&btnG=Search&as_sdt=1,5&as_sdtp=on#0
  20. Field C, Mooney HA (1983) Leaf age and seasonal effects on light, water, and nitrogen use efficiency in a California shrub. Oecologia 56(2–3):348–355CrossRefGoogle Scholar
  21. Foley JA, Prentice IC, Ramankutty N, Levis S, Pollard D, Sitch S, Haxeltine A (1996) An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochem Cycles 10(4):603–628CrossRefGoogle Scholar
  22. GCOS (2006) Systematic observation requirements for satellite-based products for climate. WMO/TD No. 1338 p 103. http://www.wmo.ch/web/gcos/gcoshome.html
  23. Ganguly S, Nemani RR, Zhang G, Hashimoto H, Milesi C, Michaelis A, Wang W et al (2012) Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration. Remote Sens Environ 122:185–202. doi: 10.1016/j.rse.2011.10.032 CrossRefGoogle Scholar
  24. Ganguly S, Samanta A, Schull MA, Shabanov NV, Milesi C, Nemani RR, Knyazikhin Y et al (2008a) Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation, analysis and validation. Remote Sens Environ 112(12):4318–4332. doi: 10.1016/j.rse.2008.07.013 CrossRefGoogle Scholar
  25. Ganguly S, Schull M, Samanta A, Shabanov N, Milesi C, Nemani R, Knyazikhin Y et al (2008b) Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory. Remote Sens Environ 112(12):4333–4343. doi: 10.1016/j.rse.2008.07.014 CrossRefGoogle Scholar
  26. Garrigues S, Lacaze R, Baret F, Morisette JT, Weiss M, Nickeson JE, Fernandes R et al (2008) Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. J Geophys Res 113(G2):G02028. doi: 10.1029/2007JG000635 CrossRefGoogle Scholar
  27. Hansen M, Roy D, Lindquist E, Adusei B, Justice C, Altstatt A (2008) A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sens Environ 112(5):2495–2513. doi: 10.1016/j.rse.2007.11.012 CrossRefGoogle Scholar
  28. Houborg R, Soegaard H, Boegh E (2007) Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sens Environ 106(1):39–58. doi: 10.1016/j.rse.2006.07.016 CrossRefGoogle Scholar
  29. Huang D, Knyazikhin Y, Dickinson RE, Rautiainen M, Stenberg P, Disney M, Lewis P et al (2007) Canopy spectral invariants for remote sensing and model applications. Remote Sens Environ 106(1):106–122CrossRefGoogle Scholar
  30. Huang D, Yang WZ, Tan B, Rautiainen M, Zhang P, Hu JN, Shabanov NV et al (2006) The importance of measurement errors for deriving accurate reference leaf area index maps for validation of moderate-resolution satellite LAI products. IEEE Trans Geosci Remote Sens 44(7):1866–1871. doi: 10.1109/tgrs.2006.876025 CrossRefGoogle Scholar
  31. Jinjun J (1995) A climate-vegetation interaction model: simulating physical and biological processes at the surface. J Biogeogr 22:445–451CrossRefGoogle Scholar
  32. Kauwe MGD, Disney MI, Quaife T, Lewis P, Williams M (2011) An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest. Remote Sens Environ 115(2):767–780. doi: 10.1016/j.rse.2010.11.004 CrossRefGoogle Scholar
  33. Knorr W, Kattge J (2005) Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling. Glob Change Biol 11(8):1333–1351. doi: 10.1111/j.1365-2486.2005.00977.x CrossRefGoogle Scholar
  34. Knyazikhin Y, Martonchik JV, Myneni RB, Diner DJ, Running SW (1998) Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J Geophys Res 103(D24):32257–32275. doi: 10.1029/98JD02462 CrossRefGoogle Scholar
  35. Knyazikhin Y, Schull MA, Xu L, Myneni RB, Samanta A (2011) Canopy spectral invariants. Part 1: A new concept in remote sensing of vegetation. J Quant Spectrosc Radiat Transfer 112(4):727–735. doi: 10.1016/j.jqsrt.2010.06.014
  36. Lewis P (1999) Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System. Agronomie 19(3–4):185–210CrossRefGoogle Scholar
  37. Lewis P, Disney M (2007) Spectral invariants and scattering across multiple scales from within-leaf to canopy. Remote Sens Environ 109(2):196–206CrossRefGoogle Scholar
  38. Li XW, Strahler AH (1992) Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy–effect of crown shape and mutual shadowing. IEEE Trans Geosci Remote Sens 30(2):276–292CrossRefGoogle Scholar
  39. Lu LX, Shuttleworth WJ (2002) Incorporating NDVI-derived LAI into the climate version of RAMS and its impact on regional climate. J Hydrometeorology 3(3):347–362CrossRefGoogle Scholar
  40. McCallum I, Wagner W, Schmullius C, Shvidenko A, Obersteiner M, Fritz S, Nilsson S (2010) Comparison of four global FAPAR datasets over Northern Eurasia for the year 2000. Remote Sens Environ 114(5):941–949. doi: 10.1016/j.rse.2009.12.009 CrossRefGoogle Scholar
  41. Melillo JM, McGuire AD, Kicklighter DW, Moore B, Vorosmarty CJ, Schloss AL (1993) Global climate-change and terrestrial net primary production. Nature 363(6426):234–240CrossRefGoogle Scholar
  42. Monteith JL, Unsworth MH (1990) Principles of environmental physics (p 291). Edward Arnold, LondonGoogle Scholar
  43. Morisette JT, Baret F, Privette JL, Myneni RB, Nickeson JE, Garrigues S, Shabanov NV et al (2006) Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup. IEEE Trans Geosci Remote Sens 44(7):1804–1817. doi: 10.1109/TGRS.2006.872529 CrossRefGoogle Scholar
  44. Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y et al (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83(1–2):214–231CrossRefGoogle Scholar
  45. 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):698–702. doi: 10.1038/386698a0 CrossRefGoogle Scholar
  46. Myneni RB, Ross J, Asrar G (1989) A review on the theory of photon transport in leaf canopies. Agric For Meteorol 45(1–2):1–153CrossRefGoogle Scholar
  47. Myneni RB, Williams DL (1994) On the relationship between FAPAR and NDVI. Remote Sens Environ 49(3):200–211CrossRefGoogle Scholar
  48. Nemani R, Pierce L, Running S, Band L (1993) Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed Leaf Area Index estimates. Int J Remote Sens 14(13):2519–2534. doi: 10.1080/01431169308904290 CrossRefGoogle Scholar
  49. Pisek J, Chen JM (2007) Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America. Remote Sens Environ 109(1):81–94. doi: 10.1016/j.rse.2006.12.004 CrossRefGoogle Scholar
  50. Pitman AJ (2003) The evolution of, and revolution in, land surface schemes designed for climate models. Int J Climatol 23(5):479–510. doi: 10.1002/joc.893 CrossRefGoogle Scholar
  51. Rautiainen, M (2005) Retrieval of leaf area index for a coniferous forest by inverting a forest reflectance model. Remote Sens Environ 99:295–303Google Scholar
  52. Ross JK, Marshak AL (1988) Calculation of canopy bidirectional reflectance using the Monte-Carlo method. Remote Sens Environ 24(2):213–225CrossRefGoogle Scholar
  53. Running SW, Gower ST (1991) Forest-BGC, a general-model of forest ecosystem processes for regional applications.2. Dynamic carbon allocation and nitrogen budgets. Tree Physiol 9(1–2):147–160CrossRefGoogle Scholar
  54. Sea WB, Choler P, Beringer J, Weinmann RA, Hutley LB, Leuning R (2011) Documenting improvement in leaf area index estimates from MODIS using hemispherical photos for Australian savannas. Agric For Meteorol 151(11):1453–1461. doi: 10.1016/j.agrformet.2010.12.006 CrossRefGoogle Scholar
  55. Sellers PJ, Mintz Y, Sud YC, Dalcher A (1986) A simple biosphere model (sib) for use within general-circulation models. J Atmos Sci 43(6):505–531CrossRefGoogle Scholar
  56. Sellers PJ, Randall DA, Collatz GJ, Berry JA, Field CB, Dazlich DA, et al. (1996) A revised land surface parameterization (SiB2) for atmospheric GCMs. Part1: Model formulation. J Clim 9(4):676–705 Google Scholar
  57. Shabanov NV, Huang D, Yang W, Tan B, Knyazikhin Y, Myneni RB, Ahl DE et al (2005) Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests. IEEE Trans Geosci Remote Sens 43(8):1855–1865. doi: 10.1109/TGRS.2005.852477 CrossRefGoogle Scholar
  58. Smolander S, Stenberg P (2005) Simple parameterizations of the radiation budget of uniform broadleaved and coniferous canopies. Remote Sens Environ 94(3):355–363. doi: 10.1016/j.rse.2004.10.010 CrossRefGoogle Scholar
  59. Steltzer H, Welker JM (2006) Modeling the effect of photosynthetic vegetation properties on the NDVI–LAI relationship. Ecology 87(11):2765–2772CrossRefGoogle Scholar
  60. Stenberg Pauline, Rautiainen Miina, Manninen T (2004) Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica 38:3–14Google Scholar
  61. Tan B, Hu JN, Huang D, Yang WZ, Zhang P, Shabanov NV, Knyazikhin Y et al (2005) Assessment of the broadleaf crops leaf area index product from the Terra MODIS instrument. Agric For Meteorol 135(1–4):124–134. doi: 10.1016/j.agrformet.2005.10.008 CrossRefGoogle Scholar
  62. Tarnavsky E, Garrigues S, Brown M (2008) Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sens Environ 112(2):535–549. doi: 10.1016/j.rse.2007.05.008 CrossRefGoogle Scholar
  63. Tian, Y. (2004) Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model. J Geophys Res 109(D1), D01103. doi: 10.1029/2003JD003777 American Geophysical Union
  64. Van Leeuwen W, Orr B, Marsh S, Herrmann S (2006) Multi-sensor NDVI data continuity: uncertainties and implications for vegetation monitoring applications. Remote Sens Environ 100(1):67–81. doi: 10.1016/j.rse.2005.10.002 CrossRefGoogle Scholar
  65. Verger A, Baret F, Weiss M, Lacaze R, Makhmara H, and Vermote E (2012) Long term consistent global GEOV1 AVHRR biophysical products. 1st EARSeL workshop on temporal analysis of satellite images, Mykonos (Greece) (pp 1–6)Google Scholar
  66. Verger Aleixandre, Baret F, Weiss M (2011) A multisensor fusion approach to improve LAI time series. Remote Sens Environ 115(10):2460–2470. doi: 10.1016/j.rse.2011.05.006 CrossRefGoogle Scholar
  67. Wang Y (2004) Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland. Remote Sens Environ 91(1):114–127. doi: 10.1016/j.rse.2004.02.007 CrossRefGoogle Scholar
  68. Wang Y, Tian Y, Zhang Y, El-Saleous N, Knyazikhin Y, Vermote E, Myneni RB (2001) Investigation of product accuracy as a function of input and model uncertainties Case study with SeaWiFS and MODIS LAI/FPAR algorithm. Remote Sens Environ 78(3):299–313. doi: 10.1016/S0034-4257(01)00225-5 CrossRefGoogle Scholar
  69. Weiss M, Baret F, Smith GJ, Jonckheere I, Coppin P (2004) Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agric For Meteorol 121:37–53CrossRefGoogle Scholar
  70. Weiss M, Baret F, Garrigues S, Lacaze R (2007) LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sens Environ 110(3):317–331. http://www.sciencedirect.com/science/article/B6V6V-4NKB1YP-1/2/7fc55dac10c40359f085241851a6fa37
  71. Welles JM, Norman JM (1991) Instrument for indirect measurement of canopy architecture. Agron J 83(5):818–825CrossRefGoogle Scholar
  72. Wulder M, Loubier E, Richardson D (2002) Landsat-7 ETM + orthoimage coverage of Canada. Can J Remote Sens 28(5):667–671CrossRefGoogle Scholar
  73. Yang W, Tan B, Huang D, Rautiainen M, Shabanov NV, Wang Y, Privette JL et al (2006) MODIS leaf area index products: from validation to algorithm improvement. IEEE Trans Geosci Remote Sens 44(7):1885–1898. doi: 10.1109/TGRS.2006.871215 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sangram Ganguly
    • 1
  • Ramakrishna R. Nemani
    • 3
  • Frederic Baret
    • 4
  • Jian Bi
    • 2
  • Marie Weiss
    • 4
  • Gong Zhang
    • 1
  • Cristina Milesi
    • 5
  • Hirofumi Hashimoto
    • 5
  • Arindam Samanta
    • 6
  • Aleixandre Verger
    • 4
  • Kumaresh Singh
    • 7
  • Ranga B. Myneni
    • 2
  1. 1.Bay Area Environmental Research Institute (BAERI)/NASA Ames Research CenterMoffett FieldUSA
  2. 2.Department of Earth and EnvironmentBoston UniversityBostonUSA
  3. 3.NASA Advanced Supercomputing DivisionMoffett FieldUSA
  4. 4.INRA-EMMAHAvignonFrance
  5. 5.Department of Science and Environmental PolicyCalifornia State University at Monterey Bay/NASA Ames Research CenterMoffett FieldUSA
  6. 6.Atmospheric and Environmental Research (AER) Inc.LexingtonUSA
  7. 7.Risk Management SolutionsNewarkUSA

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