Leaf-Area Index

by Megan Kanaga Creutzburg

Other Names:



Leaf area index (LAI) is the ratio of upper leaf surface area to ground area (for broadleaf canopies), or projected conifer needle surface area to ground area (for coniferous plants) for a given unit area. LAI directly quantifies canopy structure, and can be used to predict primary productivity and crop growth. It is commonly used in ecosystem models because it has an important influence on exchanges of energy, water vapor and carbon dioxide between plants and the atmosphere. Hence, many ecosystem process models require LAI as an input variable. LAI can be measured on the ground by harvesting leaf tissue and quantifying the leaf surface area or by various indirect techniques, such as hemispherical photography or the use of optical instruments (Plant Canopy Analyzer, DEMON, ceptometer, etc). However, over large areas it is useful to estimate LAI from remotely sensed images. LAI can be a parameter of interest on its own, or can be used as an input to models of primary productivity and fire dynamics.

LAI can be generated from satellite images using various methods, including:

  • Linear Modeling – linear modeling approaches attempt to relate reflectance data recorded by a sensor to field measurements of LAI using linear regression techniques. Such approaches may correlate field-measured fractional cover with sensor reflectance bands, or to vegetation indices like NDVI (eg. Law & Waring 1994)
  • Physical Models – Physical models use principles of how light energy is absorbed or reflected from different surfaces to estimate physical characteristics of vegetation such as fractional cover, LAI and fPAR. Biophysical models incorporate parameters related to how light interacts with processes like photosynthesis, evapotranspiration, stress, and decay of plant material.
  • Artificial Neural Networks (ANN) – ANN are networks of simple processes, decisions, or algorithms applied to data that are good at analyzing data from non-linear and non-parametric systems (see Trombetti et al. 2008)

These methods require correction for atmospheric variation and sometimes require bidirectional reflectance normalization. The images are composited over multiple days (i.e., the value for any given pixel in the final image is taken from the highest-quality readings for that pixel across multiple images) to minimize the impact of atmosphere and screening by clouds or snow. The relationship between satellite measures of reflectance and estimates of LAI will vary depending on the type of vegetation being considered, and thus major land cover type is an important input to calculating LAI. Satellite measurements of reflected radiation are often used to estimate the LAI values that are used as an intermediate variable in models of NPP.

Similar Methods


Remote sensing LAI methods generate a map of dimensionless LAI values assigned to each pixel. Values can range from 0 (bare ground) to 6 or more, but since rangeland vegetation is generally sparse, values commonly range from 0-1. A LAI value of 1 means that there is the equivalent of 1 layer of leaves that entirely cover a unit of ground surface area, and less than one means that there is some bare ground between vegetated patches. LAI values over 1 indicate a layered canopy with multiple layers of leaves per unit ground surface area. LAI and fPAR data are commonly packaged together (e.g., MODIS products).

Successful Rangeland Uses

LAI is an important input into many ecosystem models. These models can be used to characterize:

  • Primary productivity
  • Canopy complexity and structure
  • Gas-vegetation exchange processes: photosynthesis, evaporation / transpiration, interception of rainfall, carbon flux
  • Evaluation of vegetation stress due to drought, defoliation, etc.
  • Land cover change
  • Effects of climate change on vegetation communities
  • Effects of disturbance on vegetation communities

Rangeland applications include:

  • Fang et al. (2005) used LAI, along with EVI, NDVI, fPAR, and other measurements, to assess vegetation recovery from anthropogenic disturbance.
  • Hunt et al. (2003) provided an overview of the utility of various remote sensing methods, including LAI, for rangeland management.
  • Smith et al. (2006) described the use of LAI in estimating and predicting crop and rangeland yield.

Application References

  • Fang, H., S. Liang, M.P. McClaran, W.J. D. van Leeuwen, S. Drake, S.E. Marsh, A.M. Thomson, R.C. Izaurralde, and N.J. Rosenberg. 2005. Biophysical Characterization and Management Effects on Semiarid Rangeland Observed From Landsat ETM+ Data. IEEE Transactions on Geoscience and Remote Sensing 43: 125-134.
  • Hunt, Jr. E.R., J.H. Everitt, J.C. Ritchie, M.S. Moran, D.T. Booth, G.L. Anderson, P.E. Clark, and M.S. Seyfried. 2003. Applications and Research Using Remote Sensing for Rangeland Management. Photogrammetric Engineering & Remote Sensing 69: 675–693.
  • Smith, A.M., G. Bourgeois, R. DeJong, C. Nadeau, J. Freemantle, P.M. Teillet, A. Chichagov, G. Fedosejevs, H. When, and A. Shankaie. 2006. Remote Sensing Derived Leaf Area Index and Potential Applications for Crop Modeling. Geoscience and Remote Sensing Symposium, IGARSS 2006. IEEE International Conference, July 31 2006-Aug. 4 2006. Pp 2088-2091.

Technical References

  • Carlson, T. N., and D. A. Ripley. 1997. On the relation between NDVI, fractional cover, and leaf area index. Remote Sensing of Environment 62: 241-252.
  • Gower, S.T., Kucharki, C.J., and Norman, J.M. 1999. Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems. Remote Sensing of Environment 70: 29-51.
  • Hu, J., Y. Su, B. Tan, D. Huang, W. Yang, M. Schull, M.A. Bull, J.V. Martonchik, D.J. Diner, Y. Knyazikhin, and R.B. Myeni. 2007. Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy, and consistency. Remote Sensing of Environment 107: 334-347.
  • Law, B.E. and R.H. Waring. 1994. Remote sensing of leaf area index and radiation intercepted by understory vegetation. Ecological Applications 42: 272-279.
  • Morsdorf, F., B. Kotz, E. Meier, K. O. Itten, and B. Allgower. 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment 104: 50-61.
  • North, P. R. J. 2002. Estimation of fAPAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment 80: 114-121.
  • Qi, J., Y.H. Kerr, M.S. Moran, M. Weltz, A.R. Huete, S. Sorooshian, and R. Bryant. 2000. Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region. Remote Sensing of Environment 73: 18-30.
  • Propastin, P. and M. Kappas. 2009. Mapping leaf area index over semi-desert and steppe biomes in Kazakhstan using satellite imagery and ground measurements. EARSeL eProceedings 8, 1/2009.
  • Scurlock, J.M.O., G.P. Asner, and S.T. Gower. 2001. Worldwide historical estimates of leaf area index, 1932-2000. Report Prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, ORNL/TM-2001/268.
  • Tian, Y., Dickinson, R., Zhou, L., Zeng, X., Dia, Y., Myneni, R., Knyazikhin, Y., Zhang X., Friedl, M.A., Yu, H., Wanru, W. and M. Shaikh (2004). Comparison of seasonal and spatial variations of LAI/FPAR from MODIS and the common land model. Journal of Geophysical Research. Atmospheres: 109(D1). doi 10.1029/
  • Trombetti, M., D. Riaño, M.A. Rubio, Y.B. Cheng, and S.L. Ustin. 2008. Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental US. Remote Sensing of Environment 112: 203-215.
  • Wan, H., J. Wang, S. Liang, H. Fang, and Z. Xiao. 2009. Estimating leaf area index by fusing MODIS and MISR data. Spectroscopy and Spectral Analysis 29: 3106-3111.
  • White, M. A., G. P. Asner, R. R. Namani, J. L. Privette, and S. W. Running. 2000. Measuring fractional cover and leaf area index in arid ecosystems: digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment 74: 45-57.
  • Zheng, G., and L.M. Moskal. 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors 9: 2719-2745.


Remotely sensed LAI estimates are only approximations of true LAI (i.e., LAI measures that would be directly obtained by stripping all leaves from an area and quantifying their surface area per unit ground area). The mathematical models used to calculate LAI vary widely, and each model contains assumptions and requires specific inputs. It is important to understand the model assumptions and assess the suitability of the model based on the available data, how well the model characterizes the vegetation compared to field measurements, and the desired output. Most models work optimally at a particular scale and in a particular ecosystem type, and the application of an existing model to a new location may require changes to the model. LAI is often derived from spectral vegetation indices, such as NDVI, but there is no single equation with a set of coefficients that can be applied to images of different surface types. Estimation of LAI by satellite imaging requires corrections for atmospheric effects, topography and diurnal variations, and values change rapidly throughout the season with changing phenology. LAI estimates from visible/near-infrared images require a cloudless, clear image, and thus LAI values are typically chosen from the best quality images over a multiple day period (often an 8 or 10-day window). For areas that are continually cloudy, the use of radaror lidarmay be necessary to assess vegetation characteristics.

Data Inputs

Remote sensing of LAI requires an image with a visible band and a near-infrared band.

Software/Hardware Requirements

Calculating LAI requires image processing and statistical/mathematical modeling software.

Additional Information

  • Wikipedia has a page on LAI, but note that most of the information on this page deals with direct and indirect measurements of LAI, not remote sensing methods: http://en.wikipedia.org/wiki/LAI
  • University of Giessen LAI page: http://www.uni-giessen.de/~gh1461/plapada/lai/lai.html
  • Running, S.W. and NTSG. 2002. Global terrestrial net primary production from MODIS: Remote sensing of the environment with Terra L’Aquila, Italy. Powerpoint presentation available online at terra.nasa.gov/Publications/laquila/PPT/MODIS_LAI_FPAR.ppt

Existing datasets

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