Spectral Indices

Let’s see how to compute built-in spectral indices with eemont!

Before anything, let’s import our modules and authenticate in Google Earth Engine:

import ee, eemont

ee.Authenticate()
ee.Initialize()

Now, we are ready to go!

Overview

The eemont package extends the ee.Image and ee.ImageCollection classes with the method index():

ee.Image

index(self[, index, G, C1, C2, L])

Computes one or more spectral indices (indices are added as bands) for an image.

ee.ImageCollection

index(self[, index, G, C1, C2, L])

Computes one or more spectral indices (indices are added as bands) for an image collection.

Supported Platforms

This method automatically computes spectral indices for the following supported satellite platforms:

Sentinel Missions

• Sentinel-2 MSI: MultiSpectral Instrument, Level-2A

• Sentinel-2 MSI: MultiSpectral Instrument, Level-1C

Landsat Missions

• USGS Landsat 8 Surface Reflectance Tier 1 and 2

• USGS Landsat 7 Surface Reflectance Tier 1 and 2

• USGS Landsat 5 Surface Reflectance Tier 1 and 2

• USGS Landsat 4 Surface Reflectance Tier 1 and 2

Important

It is highly recommended to scale the image (or image collection) before computing spectral indices. See the scale() method for more info.

List of Indices

Vegetation Indices

The following table shows the list of built-in vegetation indices:

Built-in vegetation indices.

Index	Description	Reference
BNDVI	Blue Normalized Difference Vegetation Index	Index DataBase BNDVI
CIG	Chlorophyll Index - Green	Index DataBase CIG
CVI	Chlorophyll Vegetation Index	Index DataBase CVI
EVI	Enhanced Vegetation Index	Index DataBase EVI
GBNDVI	Green-Blue Normalized Difference Vegetation Index	Index DataBase GBNDVI
GNDVI	Green Normalized Difference Vegetation Index	Index DataBase GNDVI
GRNDVI	Green-Red Normalized Difference Vegetation Index	Index DataBase GRNDVI
MNDVI	Modified Normalized Difference Vegetation Index	Index DataBase MNDVI
NDVI	Normalized Difference Vegetation Index	Index DataBase NDVI
NGRDI	Normalized Green Red Difference Index	Index DataBase NGRDI
RVI	Ratio Vegetation Index	Index DataBase RVI
SAVI	Soil-Adjusted Vegetation Index	Index DataBase SAVI

Burn Indices

The following table shows the list of built-in burn indices:

Built-in burn indices.

Index	Description	Reference
BAI	Burned Area Index	(Martín, 1998) [spanish]
BAIS2	Burned Area Index for Sentinel 2	(Filipponi, 2018)
NBR	Normalized Burn Ratio	Index DataBase NBR

Water Indices

The following table shows the list of built-in water indices:

Built-in water indices.

Index	Description	Reference
MNDWI	Modified Normalized Difference Water Index	(Xu, 2006)
NDWI	Normalized Difference Water Index	(McFeeters, 1996)

Snow Indices

The following table shows the list of built-in snow indices:

Built-in snow indices.

Index	Description	Reference
NDSI	Normalized Difference Snow Index	(Riggs et al., 1994)

List of Bands

The following table shows the list of bands used for spectral indices computation:

Bands used for spectral indices computation.

Description	Name	Sentinel-2	Landsat 8	Landsat 4, 5, 7
Aerosols	A	B1	B1
Blue	B	B2	B2	B1
Green	G	B3	B3	B2
Red	R	B4	B4	B3
Red Edge 1	RE1	B5
Red Edge 2	RE2	B6
Red Edge 3	RE3	B7
Red Edge 4	RE4	B8A
NIR	N	B8	B5	B4
SWIR 1	S1	B11	B6	B5
SWIR 2	S2	B12	B7	B7
Thermal 1	T1		B10	B6
Thermal 2	T2		B11

Warning

If the satellite platform doesn’t have the required bands for computing an index, it won’t be computed.

Usage

The index() method computes the specified spectral index and adds it as a new band.

Let’s take the Sentinel-2 SR image collection as example (remember to scale your image or image collection!):

S2 = ee.ImageCollection('COPERNICUS/S2_SR').scale()

By default, the index() method computes the NDVI:

S2withIndices = S2.index()
S2withIndices.select('NDVI')

If required, any of the above-mentioned indices can be computed by modifying the index parameter:

S2withIndices = S2.index(index = 'EVI')
S2withIndices.select('EVI')

Specific index-parameters can be changed, for example, the canopy background adjustment L is set to 1.0 for EVI, but for SAVI it can be changed to 0.5:

S2withIndices = S2.index('SAVI',L = 0.5)
S2withIndices.select('SAVI')

If more than one index is required, a list of indices can be used:

S2withIndices = S2.index(['CIG','NBR','NDWI'])
S2withIndices.select('CIG')
S2withIndices.select('NBR')
S2withIndices.select('NDWI')

Indices can also be computed for single images:

S2withIndices = S2.first().index(['GBNDVI','MNDVI','EVI'])
S2withIndices.select('GBNDVI')
S2withIndices.select('MNDVI')
S2withIndices.select('EVI')

All vegetation indices can be computed by setting index = vegetation:

S2withIndices = S2.index('vegetation')
S2withIndices.select('NDVI')
S2withIndices.select('GNDVI')
S2withIndices.select('RVI')
# ...

All burn indices can be computed by setting index = burn:

S2withIndices = S2.index('burn')
S2withIndices.select('BAI')
S2withIndices.select('BAIS2')
S2withIndices.select('NBR')

All water indices can be computed by setting index = water:

S2withIndices = S2.index('water')
S2withIndices.select('NDWI')
S2withIndices.select('MNDWI')

All snow indices can be computed by setting index = snow:

S2withIndices = S2.index('snow')
S2withIndices.select('NDSI')

If you want to compute all available indices, you can set index = all:

S2withIndices = S2.index('all')
S2withIndices.select('NDVI')
S2withIndices.select('BAI')
S2withIndices.select('NDWI')
S2withIndices.select('NDSI')
# ...