ee.Image.normalizedDifference

Computes the normalized difference between two bands. If the bands to use are not specified, uses the first two bands. The normalized difference is computed as (first − second) / (first + second). Note that the returned image band name is 'nd', the input image properties are not retained in the output image, and a negative pixel value in either input band will cause the output pixel to be masked. To avoid masking negative input values, use ee.Image.expression() to compute normalized difference.

Usage	Returns
Image.normalizedDifference(bandNames)	Image

Argument	Type	Details
this: input	Image	The input image.
bandNames	List, default: null	A list of names specifying the bands to use. If not specified, the first and second bands are used.

Examples

Code Editor (JavaScript)

// A Landsat 8 surface reflectance image.
var img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508');

// Calculate normalized difference vegetation index: (NIR - Red) / (NIR + Red).
var nirBand = 'SR_B5';
var redBand = 'SR_B4';
var ndvi = img.normalizedDifference([nirBand, redBand]);

// Display NDVI result on the map.
Map.setCenter(-122.148, 37.377, 11);
Map.addLayer(ndvi, {min: 0, max: 0.5}, 'NDVI');

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# A Landsat 8 surface reflectance image.
img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508')

# Calculate normalized difference vegetation index: (NIR - Red) / (NIR + Red).
nir_band = 'SR_B5'
red_band = 'SR_B4'
ndvi = img.normalizedDifference([nir_band, red_band])

# Display NDVI result on the map.
m = geemap.Map()
m.set_center(-122.148, 37.377, 11)
m.add_layer(ndvi, {'min': 0, 'max': 0.5}, 'NDVI')
m