Course 2C - Calculating Spectral Indices from Satellite Imagery for Free

In order to calculate spectral indices, such as the popular vegetation index, NDVI, or our recommended vegetation index, WDRI, imagery needs to first be converted to surface reflectance. You can use the free GIS Ag Maps tutorials available on the Landsat & Sentinel-2 Surface Reflectance Guides drop-down menu - the tutorials will take you step-by-step through the process of converting free imagery with free QGIS software (or other software, such as ArcGIS).

After imagery is converted to surface reflectance, the simple task of converted imagery by using the Raster Calculator (all explained in tutorials). Below, we have included our recommended indices and methods of applying imagery to crops/vegetation, wildfire, and snow. If you need help getting started with free QGIS, see the Free Courses page. The information included below is also at the end of the QGIS Landsat & Sentinel-2 Surface Reflectance Tutorials (courses), as well as the ArcGIS Sentinel-2 tutorial. There is a vast amounts of spectral indices for a wide array of purposes; a very small fraction is included below. If there is a particular topic not included below, do an internet search and research an index with a valid source.

CALCULATING SPECTRAL INDICES (Equations)

Surface reflectance is necessary to calculate indices. After imagery has been converted to surfaces reflectance, calculating indices is a simple task using the Raster Calculator.

VEGETATION

(It is important to complete or review Course 3A to understand vegetation spectral indices.)

Plant Biomass and Vigor Indices

We recommend Wide Dynamic Randge Index (WDRI) (Gitelson, 2004) for crops and other vegetation. An advantage of WDRI is that it tends to more equally weight red and NIR surface reflectance. For healthy green vegetation, NIR surface reflectance is roughly 10 times greater than red surface reflectance (multiplying NIR by a 0.1 factor helps produce more equal NIR and red values).

WDRI can be written as follows (all bands are in surface reflectance): ([NIR * 0.1] - Red) / ([NIR * 0.1] + Red)

For Sentinel-2, use Band 8a NIR.

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Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1973) is the same as WDRI, except the 0.1 value is not applied. An advantage of NDVI is that it is the most researched and well-documented.

NDVI is as follows (all bands are in surface reflectance): (NIR - Red) / (NIR + Red)

For Sentinel-2, use Band 8a NIR.

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Plant Water Content Index

Normalized Difference Water Index (NDWI) (Gao, 1996) represents plant water content and is written as follows (all bands are in surface reflectance):

(SWIR - NIR) / (SWIR + NIR)

For Landsat 8, use Band 6 SWIR; for Landsat 7, 5, and 4, use Band 5 SWIR; for Sentinel-2, use Band 8a NIR and Band 11 SWIR.

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Red Edge Vegetation Indices (applies to Sentinel-2 imagery)

The red edge is the spectral region where vegetation reflectance abruptly increases from red to NIR. Course 3A shows that Sentinel-2 Band 5 (lower region of red edge) can positively correlate to Band 6 and Band 7 red edge. Clevers and Gitelson (2013) found there would be a high correlation between total crop and grass chlorophyll and nitrogen content based on the Sentinel-2 red edge Band 6:Band 5 ratio (research was completed prior to Sentinel-2 acquiring imagery by deriving information based on another satellite platform).

Use an equation from the publication accessed through the previous link or get started by simply dividing (in surface reflectance) Sentinel-2 Band 6 by Band 5 with the Raster Calculator; also try Band 7 divided by Band 5 (Course 3A shows there is also a high negative correlation between Band 7 and Band 5). The value of red edge applied to vegetation is quite well-documented.

WILDFIRE

The extent of a wildfire is mapped with the Differenced Normalized Burn Ratio (dNBR) which is as follows (in surface reflectance):

dNBR = NBRprefire - NBRpostfire

where, NBR = (NIR - SWIR) / (NIR + SWIR)

(For Landsat 5, 7, and 8 use Band 7 SWIR; for Sentinel-2, use Band 8a NIR and Band 12 SWIR)

SNOW

The extent of snow can be mapped with the Normalized Difference Snow Index (NDSI) (Dozier, 1989). NDSI is as follows (in surface reflectance):

(Green - SWIR) / (Green + SWIR)

(For Landsat 4, 5, and 7; and 5 use Band 5 SWIR. For Landsat 8, use Band 6 SWIR; For Sentinel-2, use Band 11 SWIR)

Though NDSI has been used to map snow, for small scale-areas we recommend using Sentinel-2 green band solely (though the blue and red band also work well) because of the fine 10-meter resolution. See the snow mapping page on this website for an example.

References

Chavez, P.S., Jr. 1996. Image-based atmospheric corrections–revisited and improved. Photogrammetric Engineering and Remote Sensing 62(9): pp.1025-1036.

Chavez, P.S., Jr. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24: pp.459-479.

Clevers, J.G.P.W. and A.A. Gitelson. 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoinformation 23: pp. 344–351.

Dozier, J. 1989. Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sensing of Environment 28: pp. 9-22.

Gao, B.C. 1996. NDWI -  A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58: pp. 257-266.

Gitelson, A.A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 161; pp. 165–173.

Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering (1973). Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351 I, 309-317.