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