Journal of the Indian Society of Remote Sensing

, Volume 42, Issue 4, pp 733–743 | Cite as

Leaf Area Index Estimation Using Time-Series MODIS Data in Different Types of Vegetation

  • Shenghui Fang
  • Yuan Le
  • Qi Liang
  • Xiaojun Liu
Research Article


The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square = 0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites.


LAI Time-series MODIS data Vegetation index Neural network 



This research is supported by National High Technology Research and Development Program of China(863 Program)(grand No. 2013AA102401 and 2012AA12A304).


  1. Abuelgasim, A. A., Gopal, S., & Strahler, A. H. (1998). Forward and inverse modelling of canopy directional reflectance using a neural network. International Journal of Remote Sensing, 19(3), 453–471.CrossRefGoogle Scholar
  2. Aragao, L. E. O. C., Shimabukuro, Y. E., Espírito-Santo, F. D. B., & Williams, M. (2005). Spatial validation of the collection 4 MODIS LAI product in Eastern Amazonia. IEEE Transactions on Geoscience and Remote Sensing, 43, 2526–2534.CrossRefGoogle Scholar
  3. Atzberger, C. (2010). Inverting the PROSAIL canopy reflectance model using neural nets trained on streamlined databases. Journal of Spectral Imaging, 1(1), 1–13.Google Scholar
  4. Bacour, C., Baret, F., Beal, D., Weiss, M., & Pavageau, K. (2006). Neural network estimation of LAI, fAPAR, fCover and LAI × Cab, from top of canopy MERIS reflectance data: principles and validation. Remote Sensing of Environment, 105(4), 313–325.CrossRefGoogle Scholar
  5. Baret, F. (1995). Use of spectral reflectance variation to retrieve canopy biophysical characteristics. In F. M. Danson & S. E. Plummer (Eds.), Advances in environmental remote sensing (pp. 33–51). Chichester: Wiley.Google Scholar
  6. Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2-3), 161–173.CrossRefGoogle Scholar
  7. Baret, F., Clevers, J. G. P. W., & Steven, M. D. (1995). The robustness of canopy gap fraction estimates from red and near-infrared reflectances: a comparison of approaches. Remote Sensing of Environment, 54, 141–151.CrossRefGoogle Scholar
  8. Bonan, G. B. (1993). Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sensing of Environment, 43(3), 303–314.CrossRefGoogle Scholar
  9. Buschmann, C., & Nagel E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14, 711–722.Google Scholar
  10. Chen, X., Vierling, L., Deering, D., & Conley, A. (2005). Monitoring boreal forest leaf area index across a Siberian burn chronosequence: A MODIS validation study. International Journal of Remote Sensing, 26, 5433–5451.CrossRefGoogle Scholar
  11. Chen, S., Fang, L., Li, H., Chen, W., & Huang, W. (2011). Evaluation of a three-band model for estimating chlorophyll-a concentration in tidal reaches of the Pearl River Estuary, China. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 356–364.CrossRefGoogle Scholar
  12. Cho, M. A., & Skidmore, A. K. (2006). A new technique for extracting the Red edge position from hyperspectral data: the linear extrapolation method. Remote Sensing of Environment, 101(2), 181–193.CrossRefGoogle Scholar
  13. Cho, M. A., Skidmore, A. K., & Atzberger, C. (2008). Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties Optique Spectrales des Feuilles (PROSPECT) and Scattering by Arbitrarily Inclined Leaves (SAILH) simulated data. International Journal of Remote Sensing, 29(8), 2241–2255.CrossRefGoogle Scholar
  14. Cohen, W. B., Maiersperger, T. K., Gower, S. T., & Turner, D. P. (2003). An improved strategy for regression of biophysical variables and Landsat ETM + data. Remote Sensing of Environment, 84, 561–571.CrossRefGoogle Scholar
  15. Cohen, W. B., Maiersperger, T. K., Turner, D. P., Ritts, W. D., Pflugmacher, D., Kennedy, R. E., Kirschbaum, A., Running, S. W., Costa, M., & Gower, S. T. (2006). MODIS land cover and LAI collection 4 product quality across nine sites in the Western Hemisphere. IEEE Transactions on Geoscience and Remote Sensing, 44, 1843–1857.Google Scholar
  16. Danson, F. M., Rowland, C. S., & Baret, F. (2003). Training a neural network with a canopy reflectance model to estimate crop leaf area index. International Journal of Remote Sensing, 24(23), 4891–4905.CrossRefGoogle Scholar
  17. Darvishzadeh, R., Atzberger, C., Skidmore, A. K., & Abkar, A. A. (2009). Leaf area index derivation from hyperspectral vegetation indices and the red edge position. International Journal of Remote Sensing, 30(23), 6199–6218.CrossRefGoogle Scholar
  18. De Kauwe, M. G., Disney, M. I., 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 Sensing of Environment, 115(2), 767–780.CrossRefGoogle Scholar
  19. Doraiswamy, P., Hatfield, J., Jackson, T., Akhmedov, B., Prueger, J., & Stern, A. (2004). Crop condition and yield simulation using Landsat and MODIS. Remote Sensing of Environment, 92(4), 548–559.CrossRefGoogle Scholar
  20. Fang, H., & Liang, S. (2005). A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sensing of Environment, 94, 405–424.CrossRefGoogle Scholar
  21. Fang, H. L., Wei, S. S., Jiang, C. Y., & Scipal, K. (2012a). Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sensing of Environment, 124, 610–621.CrossRefGoogle Scholar
  22. Fang, H. L., Wei, S. S., & Liang, S. L. (2012b). Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Environment, 119(16), 43–54.CrossRefGoogle Scholar
  23. Fensholt, R., Sandholt, I., & Rasmussen, M. S. (2004). Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91, 490–507.CrossRefGoogle Scholar
  24. Gitelson, A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology, 148, 494–500.Google Scholar
  25. Gitelson, A. A., Gritz, U., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282.CrossRefGoogle Scholar
  26. Goel, N. S. (1989). Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data. In G. Asrar (Ed.), Theory and applications of optical remote sensing (pp. 205–251). New York: Wiley & Sons.Google Scholar
  27. Gower, S. T., & Norman, J. M. (1991). Rapid estimation of leaf area index in conifer and broad-leaf plantations. Ecological Society of America, 72(5), 1896–1990.Google Scholar
  28. Gower, S. T., Kucharik, C. J., & Norman, J. M. (1999). Direct and indirect estimation of Leaf Area Index, F(Apar), and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70, 29–51.CrossRefGoogle Scholar
  29. Haboudane, D., Tremblay, N., Miller, J. R., & Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 423–437.CrossRefGoogle Scholar
  30. Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4), 542–553.CrossRefGoogle Scholar
  31. Heinsch, F. A., Zhao, M. S., Running, S. W., Kimball, J. S., Nemani, R. R., Davis, K. J., Bolstad, P. V., Cook, B. D., Desai, A. R., Ricciuto, D. M., Law, B. E., Oechel, W. C., Kwon, H., Luo, H., Wofsy, S. C., Dunn, A. L., Munger, J. W., Baldocchi, D. D., Xu, L., Hollinger, D. Y., Richardson, A. D., Stoy, P. C., Siqueira, M. B. S., Monson, R. K., Burns, S. P., & Flanagan, L. B. (2006). Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing, 44(7), 1908–1925.Google Scholar
  32. Houborg, R., & Boegh, E. (2008). Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data. Remote Sensing of Environment, 112(1), 186–202.CrossRefGoogle Scholar
  33. 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 Sensing of Environment, 106(1), 39–58.CrossRefGoogle Scholar
  34. Jensen, J. L. R., Humes, K. S., Hudak, A. T., Vierling, L. A., & Delmell, E. (2011). Evaluation of the MODIS LAI product using independent lidar-derived LAI: a case study in mixed conifer forest. Remote Sensing of Environment, 115(12), 3625–3639.CrossRefGoogle Scholar
  35. Jiang, B., Liang, S. L., Wang, J. D., & Xiao, Z. Q. (2010). Modeling MODIS LAI time series using three statistical methods. Remote Sensing of Environment, 114(7), 1432–1444.CrossRefGoogle Scholar
  36. Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., & Morisette, J. T. (2002). An overview of MODIS land data processing and product status. Remote Sensing of Environment, 83(1–2), 3–15.Google Scholar
  37. Knyazikhin, Y., Martonchik, J. V., Myneni, R. B., Dinner, D. J., & Running, S. W. (1998). Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research, 103, 32257–32276.CrossRefGoogle Scholar
  38. Krasnopolsky, V. M., & Chevallier, F. (2003). Some neural network applications in environmental sciences. Part II: advancing computational efficiency of environmental models. Neural Networks, 16, 335–348.CrossRefGoogle Scholar
  39. Lichtenthaler, H., Gitelson, A., & Lang, M. (1996). Non-destructive determination of chlorophyll concentration of leaves of a green and an aurea mutant of tobacco by reflectance measurements. Journal of Plant Physiology, 148, 483–493.Google Scholar
  40. Maire, G., François, C., Soudani, K., Berveiller, D., Pontailler, J., Bréda, N., Genet, H., Davi, H., & Dufrêne, E. (2008). Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112(10), 3846–3864.Google Scholar
  41. Maire, G., Marsden, C., Verhoef, W., Ponzoni, F. J., Seen, D., Bégué, A., Stape, J-L., & Nouvellon, Y. (2011). Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of eucalyptus plantations. Remote Sensing of Environment, 115(2), 586–599.Google Scholar
  42. Mutanga, O., & Skidmore, A. K. (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25(19), 3999–4014.CrossRefGoogle Scholar
  43. Running, S. W., Nemani, R. R., Peterson, D. L., Band, L. E., Potts, D. F., Pierce, L. L., & Spanner, M. A. (1989). Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology, 70(4), 1090–1101.Google Scholar
  44. Salomonson, V. V., Barnes, W., Maymon, P. W., Montgomery, H. E., & Ostrow, H. (1989). MODIS: advanced facility instrument for studies of the earth as a system. IEEE Transactions on Geoscience and Remote Sensing, 27(2), 145–153.CrossRefGoogle Scholar
  45. Schlerf, M., Atzberger, C., Hill, J., Buddenbaum, H., Werner, W., & Schüler, G. (2010). Retrieval of chlorophyll and nitrogen in Norway Spruce (PiceaAbies L. Karst.) using imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 12(1), 17–26.CrossRefGoogle Scholar
  46. Shawn, P. S., Douglas, E. A., & Stith, T. G. (2013). Spatial and temporal validation of the MODIS LAI and FPAR products across a boreal forest wildfire chronosequence. Remote Sensing of Environment, 133(15), 71–84.Google Scholar
  47. Smith, J. A. (1993). LAI inversion using a back-propagation neural network trained with a multiple scattering model. IEEE Transactions on Geoscience and Remote Sensing, 31, 1102–1106.CrossRefGoogle Scholar
  48. Tan, B., Hu, J. N., Zhang, P., Huang, D., Shabanov, N., Weiss, M., et al. (2005). Validation of moderate resolution imaging spectroradiometer leaf area index product in croplands of Alpilles, France. Journal of Geophysical Research, 110, 16.CrossRefGoogle Scholar
  49. Weiss, M., & Baret, F. (1999). Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data. Remote Sensing of Environment, 70, 293–306.CrossRefGoogle Scholar
  50. Yang, W. Z., Tan, B., Huang, D., Rautiainen, M., Shabanov, N. V., Wang, Y., Privette, J. L., Huemmrich, K. F., Fensholt, R., Sandholt, I., Weiss, M., Ahl, D. E., Gower, S. T., Nemani, R. R., Knyazikhin, Y., & Myneni, R. B. (2006). MODIS leaf area index products: from validation to algorithm improvement. IEEE Transactions on Geoscience and Remote Sensing, 44, 1885–1898.Google Scholar
  51. Yi, Y. H., Yang, D. W., Huang, J. F., & Chen, D. Y. (2008). Evaluation of MODIS surface reflectance products for wheat leaf area index (LAI) retrieval. ISPRS Journal of Photogrammetry and Remote Sensing, 63(6), 661–677.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2014

Authors and Affiliations

  1. 1.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina

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