Spectral Indices Computation

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 spectralIndices():

ee.Image

spectralIndices(self[, index, G, C1, C2, L, …])

Computes one or more spectral indices (indices are added as bands) for an image from the Awesome List of Spectral Indices.

ee.ImageCollection

spectralIndices(self[, index, G, C1, C2, L, …])

Computes one or more spectral indices (indices are added as bands) for an image collection from the Awesome List of Spectral Indices.

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

MODIS Products (Terra + Aqua)

• MCD43A4.006 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m

MODIS Products (Terra)

• MOD09GQ.006 Terra Surface Reflectance Daily Global 250m

• MOD09GA.006 Terra Surface Reflectance Daily Global 1km and 500m

• MOD09Q1.006 Terra Surface Reflectance 8-Day Global 250m

• MOD09A1.006 Terra Surface Reflectance 8-Day Global 500m

MODIS Products (Aqua)

• MYD09GQ.006 Aqua Surface Reflectance Daily Global 250m

• MYD09GA.006 Aqua Surface Reflectance Daily Global 1km and 500m

• MYD09Q1.006 Aqua Surface Reflectance 8-Day Global 250m

• MYD09A1.006 Aqua Surface Reflectance 8-Day Global 500m

Important

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

List of Indices

The list of indices is retrieved from the Awesome List of Spectral Indices for Google Earth Engine

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
EVI2	Two-Band Enhanced Vegetation Index	(Jiang et al., 2008)
GARI	Green Atmospherically Resistant Vegetation Index	Index DataBase GARI
GBNDVI	Green-Blue Normalized Difference Vegetation Index	Index DataBase GBNDVI
GEMI	Global Environment Monitoring Index	Index DataBase GEMI
GLI	Green Leaf Index	Index DataBase GLI
GNDVI	Green Normalized Difference Vegetation Index	Index DataBase GNDVI
GRNDVI	Green-Red Normalized Difference Vegetation Index	Index DataBase GRNDVI
GVMI	Global Vegetation Moisture Index	Index DataBase GVMI
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
VARI	Visible Atmospherically Resistant Index	Index DataBase VARI

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)
CSIT	Char Soil Index Thermal	(Smith et al., 2007)
NBR	Normalized Burn Ratio	Index DataBase NBR
NBRT	Normalized Burn Ratio Thermal	(Holden et al., 2005)
NDVIT	Normalized Difference Vegetation Index Thermal	(Smith et al., 2007)
SAVIT	Soil-Adjusted Vegetation Index Thermal	(Smith et al., 2007)

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)

Drought Indices

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

Built-in snow indices.

Index	Description	Reference
NDDI	Normalized Difference Drought Index	(Gu et al., 2007)

Generalized Kernel Indices

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

Built-in kernel indices.

Index	Description	Reference
kEVI	Kernel Enhanced Vegetation Index	(Camps-Valls et al., 2021)
kNDVI	Kernel Normalized Difference Vegetation Index	(Camps-Valls et al., 2021)
kRVI	Kernel Ratio Vegetation Index	(Camps-Valls et al., 2021)
kVARI	Kernel Visible Atmospherically Resistant Index	(Camps-Valls et al., 2021)

Kernels

In the case of generalized kernel indices, the following kernels are available:

Linear Kernel

The linear kernel for generalized kernel indices can be selected by setting kernel = 'linear'.

\[k(a,b) = ab\]

RBF Kernel

The Radial Basis Function (RBF) kernel for generalized kernel indices can be selected by setting kernel = 'RBF'.

\[k(a,b) = exp(- \frac{(a - b) ^ 2}{2 \sigma ^ 2})\]

Where \(\sigma\) is a free length-scale parameter.

Polynomial Kernel

The polynomial kernel for generalized kernel indices can be selected by setting kernel = 'poly'.

\[k(a,b) = (ab + c) ^ p\]

Where \(c\) is a free parameter that trades off the influence of higher-order versus lower-order terms and \(p\) is the kernel degree.

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 457	MODIS
Aerosols	A	B1	B1
Blue	B	B2	B2	B1	B3
Green	G	B3	B3	B2	B4
Red	R	B4	B4	B3	B1
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	B2
SWIR 1	S1	B11	B6	B5	B6
SWIR 2	S2	B12	B7	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 spectralIndices() 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').scaleAndOffset()

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

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

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

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

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

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

Indices can also be computed for single images:

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

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

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

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

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

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

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

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

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

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

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

Generalized Kernel Indices

Generalized kernel indices are availabe through eemont (e.g. kNDVI):

S2withIndices = S2.spectralIndices('kNDVI')
S2withIndices.select('kNDVI')

By default, the RBF kernel is used and the sigma parameter is 0.5 * (a + b) (this means, that for k(N,R), sigma = '0.5 * (N + R)'). If required, sigma can be modified by another expression (using a and b) or a float:

S2withIndices = S2.spectralIndices('kNDVI',sigma = 1)
S2withIndices.select('kNDVI')

The kernel can be modified by modifying the kernel parameter:

S2withIndices = S2.spectralIndices('kNDVI',kernel = 'poly')
S2withIndices.select('kNDVI')

For the polynomial kernel, the p and c parameters can be modified:

S2withIndices = S2.spectralIndices('kNDVI',kernel = 'poly',p = 4,c = 0)
S2withIndices.select('kNDVI')

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

S2withIndices = S2.spectralIndices('kernel')
S2withIndices.select('kEVI')
S2withIndices.select('kNDVI')
S2withIndices.select('kRVI')
S2withIndices.select('kVARI')

See also

For more info on generalized kernel indices, please visit ‘Camps-Valls, G., et al. 2021. A unified vegetation index for quantifying the terrestrial biosphere. Science Advances 7 (9): eabc7447. Doi: 10.1126/sciadv.abc7447’.

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