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Spectral Indices

Spectral index is the ratio of broadband spectral bands or as the normalized differences between two bands.

There are various hyperspectral sensors to capture hyperspectral data whose band centers are usually slightly different. Thus defining bands gives you the freedom to apply spectral indices to a wide range of sensors. The Band Definition section provide details of band definitions used in the Image Processing Toolbox™ Hyperspectral Imaging Library.

Spectral indices characterize the specific features of interest of a target by exploiting its biophysical and chemical properties. These features of interest enable you to identify plant, water, soil, and various forms of built-up regions such as road, house, railway track, and parking lot. For more information on supported spectral indices, see List of Supported Spectral Indices section.

Left: RGB image of Pavia University. Right: NDVI image of Pavia University.

Band Definition

Image Processing Toolbox Hyperspectral Imaging Library uses various band definitions to compute spectral indices. The toolbox selects the nearest wavelength to the center of each band available in input hyperspectral data.

band definition

Depending on the range of the wavelengths for a band, the band definition can be of two types.

  • Broadband — Bands generally have wider wavelength ranges.

  • Narrowband — Bands generally have narrow wavelength ranges.

This table lists the broadband definitions used in the toolbox.

B400 nm470 nm500 nm
G500 nm550 nm600 nm
R600 nm650 nm700 nm
NIR760 nm860 nm960 nm
SWIR1 1550 nm1650 nm1750 nm
SWIR22080 nm2220 nm2350 nm

This table lists the narrowband definitions used in the toolbox.

B531525 nm531 nm550 nm
B550540 nm550 nm560 nm
B570560 nm570 nm575 nm
B670650 nm670 nm690 nm
B700 680 nm700 nm730 nm
B795720 nm795 nm800 nm
B800780 nm800 nm865 nm
B819815 nm819 nm824 nm
B990830 nm990 nm995 nm
B15101500 nm1510 nm1515 nm
B15991590 nm1599 nm1620 nm
B16801670 nm1680 nm1690 nm
B20001980 nm2000 nm2040 nm
B21002085 nm2100 nm2110 nm
B22002170 nm2200 nm2220 nm

List of Supported Spectral Indices

Image Processing Toolbox Hyperspectral Imaging Library supports various spectral indices used to identify vegetation, minerals, burned areas, and built up regions. This table lists the hyperspectral indices supported by the spectralIndices function. The equations of these indices uses the band definitions as specified in Band Definition. You can compute the indices CMR, EVI, GVI, MNDWI, NBR, NDBI, NDVI, OSAVI, and SR, which use broadband band definitions, for both narrowband hyperspectral images and broadband multispectral images. You can compute the indices CAI, MCARI, MTVI, MSI, NDMI, NDNI, and PRI, which use narrowband band definitions, for only narrowband hyperspectral images.

Index NameEquationDescription
Cellulose absorption index (CAI)CAI=0.5(B2000+B2200)B2100

CAI identifies dried plant materials relative to the cellulose sensitive wavelengths in the range 2000 nm to 2200 nm. Use this index to monitor crop residue, plant health, and fuel conditions in an ecosystem.

The index value varies in the range (-3, 4).

Clay minerals ratio (CMR)CMR=SWIR1SWIR2

CMR identifies hydrothermally altered rocks containing clay and alunite. Use this index to map minerals in rock surfaces.

Enhanced vegetation index (EVI)EVI=(NIRR)(NIR+6R7.5B+1)

EVI identifies vegetation regions with a high leaf area index. It uses blue reflectance to correct the soil background by including atmospheric influences.

For vegetation pixels, the index value varies in the range (0, 1).

Green vegetation index (GVI)GVI=(0.2848B)+(0.2435G)+(0.5436R)+...(0.7243NIR)+(0.0840SWIR1)+(0.1800SWIR2)

GVI identifies green vegetation by reducing the background soil effect.

The index value varies in the range (-1, 1).

Modified chlorophyll absorption ratio index (MCARI)MCARI=(B700B670)(0.2(B700B550))(B700B670)MCARI identifies the vegetation regions that contain chlorophyll by minimizing the combined effects of soil and non-photosynthetic surfaces.
Modified triangular vegetation index (MTVI)MTVI=1.2((1.2(B800B550))(2.5(B670B550)))MTVI identifies vegetation regions. This index includes the 800 nm wavelength, which is influenced by changes in leaf and canopy structure.
Modified normalized difference water index (MNDWI)MNDWI=GSWIR1G+SWIR1MNDWI identifies open water surfaces, reducing background noise from soil, vegetation, and built-up areas.
Moisture stress index (MSI)MSI=B1599B819

MSI maps the level of leaf water content in vegetation canopies.

The index value varies in the range (0, 3).

Normalized burn ratio (NBR)NBR=NIRSWIR2NIR+SWIR2NBR identifies burned areas in larger fire zones. You can obtain a burn sensitive image by subtracting the pre-fire and post-fire NBR images.
Normalized difference built-up index (NDBI)NDBI=SWIR1NIRSWIR1+NIRNDBI identifies urban areas with high reflectance in the SWIR region as compared to the NIR region.
Normalized difference mud index (NDMI)NDMI=B795B990B795+B990NDMI identifies shallow water or muddy surfaces.
Normalized difference nitrogen index (NDNI)NDNI=log(1B1510)log(1B1680)log(1B1510)+log(1B1680)NDNI maps the amount of nitrogen content in vegetation canopies. Because NDNI is a logarithmic index, use data values in the range 0 to 1 for accurate results.
Normalized difference vegetation index (NDVI)NDVI=NIRRNIR+R,

NDVI identifies vegetation canopies.

The index value varies in the range (-1, 1). A value close to 1 indicates healthy vegetation, 0 indicates unhealthy vegetation, and -1 indicates no vegetation.

Optimized soil adjusted vegetation index (OSAVI)OSAVI=(NIRR)(NIR+R+0.16)OSAVI identifies sparse vegetation, where soil is visible through the canopy.
Photochemical reflectance index (PRI)PRI=B531B570B531+B570

PRI maps the photosynthetic efficiency of a region by detecting the changes of carotenoid pigments in live foliage.

The index value varies in the range (-1, 1).

Simple ratio (SR)SR=NIRRSR finds the ratio of vegetation and chlorophyll absorption wavelength features.


[1] Bannari, A., D. Morin, F. Bonn, and A. R. Huete. “A Review of Vegetation Indices.” Remote Sensing Reviews 13, no. 1–2 (August 1995): 95–120.

[2] Xue, Jinru, and Baofeng Su. “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications.” Journal of Sensors 2017 (2017): 1–17.

[3] Haboudane, D. “Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture.” Remote Sensing of Environment 90, no. 3 (April 15, 2004): 337–52.

[4] Thenkabail, Prasad S., John G. Lyon, and Alfredo Huete, eds. Hyperspectral Indices and Image Classifications for Agriculture and Vegetation. Boca Raton: CRC Press, 2018.

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