Enhanced Vegetation Index

Other Names:



The enhanced vegetation index (EVI) was developed as an alternative vegetation index to address some of the limitations of the NDVI. The EVI was specifically developed to:

  1. be more sensitive to changes in areas having high biomass (a serious shortcoming of NDVI),
  2. reduce the influence of atmospheric conditions on vegetation index values, and
  3. correct for canopy background signals.

EVI tends to be more sensitive to plant canopy differences like leaf area index (LAI), canopy structure, and plant phenology and stress than does NDVI which generally responds just to the amount of chlorophyll present. With the launch of the MODIS sensors, NASA adopted EVI as a standard MODIS product that is distributed by the USGS (see below).

EVI is calcualted as
where NIR, RED, and BLUE are atmospherically-corrected (or partially atmospherically-corrected) surface reflectances, and C1, C2, and L are coefficients to correct for atmospheric condition (i.e., aerosol resistance). For the standard MODIS EVI product, L=1, C1=6, and C2=7.5.

Similar Methods


The output of EVI is a single image layer with values typically from 0.0 to 1.0.

Successful Rangeland Uses

Most of the rangeland applications of EVI have, to date, been regional-scale to global-scale assessments of rangeland parameters. EVI has mostly been used for assessments of biomass, biophysical properties like leaf area index, quantification of evaoptranspiration or water-use efficience, or assessments of change over large areas. In addition to the citations below, RangeView includes a standard EVI component in it’s web-based rangeland assessment tools.

Application References

  • Fang. H. S. Liang, M.P. McClaren, W. van Drake, S.E. Marsh, A.M. Thompson, R.C. Izaurralde, and N.J. Rosenberg. Biophysical characterization and management effects on semiarid rangeland observed from Landsat ETM+ data. IEEE Transactions on Geoscience and Remote Sensing 43(1):125-134.
  • Nagler, P.L., R.L. Scott, C. Westenburg, J.R. Cleverly, E.P. Glenn, and A.R. Huete. 2005. Evapotranspiration on western U.S. rivers estimated using the enhanced vegetation index from MIDOS and data from eddy covariance and Bowen ratio flux towers. Remote Sensing of the Environment 97(3):337-351.
  • Nagler, P.L., E.P. Glenn, and A.R. Huete. 2001. Assessment of spectral vegetation indices for riparian vegetation in the Colorado River delta, Mexico. Journal of Arid Environments 49(1):91-110.
  • Nagler, P.L., E.P. Glenn, H. Kim, W. Emmerich, R.L. Scott, T.E. Huxman, and A.R. Huete. 2007. Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices. Remote Sensing of the Environment 70(3):443-462.
  • Yang, Y.H., J.Y. Fang, Y.D. Pan, C.J. Yi. 2009. Aboveground biomass in Tibetan grasslands. Journal of Arid Environments 73:91-95.

Technical References

  • Huete,A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao and L.G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195–213.
  • Jiang, Z., A.R. Huete, K. Didan, and R. Miura. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of the Environment 112(10):3833-3845.
  • Justice et al., C.O. Justice, E. Vermote, J.R.G. Townshend, R. Defries, D.P. Roy and D.K. Hall. 1997 The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing 36:1228–1249.
  • Matsushita, B., W. Yang, J. Chen, Y. Onda, and G. Qiu. 2007. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors 7:2636-2651.
  • Miura, T., A.R. Huete, H. Yoshioka and B.N. Holben. 2001. An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sensing of Environment 78:284–298.


One of the biggest current limitations to implementing EVI is that it needs a blue band in order to be calculated. Not only does this limit the sensors that EVI can be applied to (e.g., ASTER has no blue band), but the blue band typically has a low signal-to-noise ratio. Research is ongoing to develop a two-band EVI that can be calculated from just red and near infrared bands (see Jiang et al. 2008).

Data Inputs

EVI requires surface reflectance measurements from blue, red, and near-infrared bands.

Software/Hardware Requirements

Given the appropriate inputs, EVI is fairly easy to calculate with image processing software (e.g., ERDAS Imagine, ENVI, IDRISI) or GIS software that can do raster processing (e.g., ArcGIS with Spatial Analyst Extension, GRASS). Most applications of EVI, however, have made use of the standard MODIS EVI products that can be downloaded from USGS LPDAAC (see below).

Sample Graphic

Image source: RangeView MODIS Dynamic Animation Tool, http://rangeview.arizona.edu
MODIS EVI (right) compared to NDVI (left) for New Mexico over the same time period in 2006. The NDVI image shows a greater area in dark green because NDVI loses sensitivity to changes in vegetation in areas of higher biomass (forests in this case). The EVI image maintains a more consistent sensitivity to changes in vegetation and, in this example, has a more even distribution of vegetation greenness values.

Additional Information

Existing datasets

  • The University of Arizona RangeView project includes MODIS EVI data products in it’s MODIS Dynamic Animation Tool. http://rangeview.arizona.edu
  • EVI is part of the MODIS Vegetation Indices product that can be downloaded from the USGS LP DAAC at: https://lpdaac.usgs.gov/.

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