ee.Image.reduceRegion

AI-generated Key Takeaways

• Image.reduceRegion() applies a reducer function to all pixels within a specified region of an image.

• The reducer can either accept the same number of inputs as the image bands or a single input to be applied to each band.

• It returns a dictionary containing the reducer's output, with keys representing band names and values corresponding to the calculated statistic.

• Users can define parameters like scale, projection, and geometry to control the region and resolution of the reduction operation.

• Multiple reducers can be combined to calculate multiple statistics simultaneously, with output dictionary keys reflecting both band and statistic names.

Apply a reducer to all the pixels in a specific region.

Either the reducer must have the same number of inputs as the input image has bands, or it must have a single input and will be repeated for each band.

Returns a dictionary of the reducer's outputs.

Usage	Returns
Image.reduceRegion(reducer, geometry, scale, crs, crsTransform, bestEffort, maxPixels, tileScale)	Dictionary

Argument	Type	Details
this: image	Image	The image to reduce.
reducer	Reducer	The reducer to apply.
geometry	Geometry, default: null	The region over which to reduce data. Defaults to the footprint of the image's first band.
scale	Float, default: null	A nominal scale in meters of the projection to work in.
crs	Projection, default: null	The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform	List, default: null	The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
bestEffort	Boolean, default: false	If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed.
maxPixels	Long, default: 10000000	The maximum number of pixels to reduce.
tileScale	Float, default: 1	A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.

Examples

Code Editor (JavaScript)

// A Landsat 8 surface reflectance image with SWIR1, NIR, and green bands.
var img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508')
              .select(['SR_B6', 'SR_B5', 'SR_B3']);

// Santa Cruz Mountains ecoregion geometry.
var geom = ee.FeatureCollection('EPA/Ecoregions/2013/L4')
               .filter('us_l4name == "Santa Cruz Mountains"').geometry();

// Display layers on the map.
Map.setCenter(-122.08, 37.22, 9);
Map.addLayer(img, {min: 10000, max: 20000}, 'Landsat image');
Map.addLayer(geom, {color: 'white'}, 'Santa Cruz Mountains ecoregion');

// Calculate median band values within Santa Cruz Mountains ecoregion. It is
// good practice to explicitly define "scale" (or "crsTransform") and "crs"
// parameters of the analysis to avoid unexpected results from undesired
// defaults when e.g. reducing a composite image.
var stats = img.reduceRegion({
  reducer: ee.Reducer.median(),
  geometry: geom,
  scale: 30,  // meters
  crs: 'EPSG:3310',  // California Albers projection
});

// A dictionary is returned; keys are band names, values are the statistic.
print('Median band values, Santa Cruz Mountains ecoregion', stats);

// You can combine reducers to calculate e.g. mean and standard deviation
// simultaneously. The output dictionary keys are the concatenation of the band
// names and statistic names, separated by an underscore.
var reducer = ee.Reducer.mean().combine({
  reducer2: ee.Reducer.stdDev(),
  sharedInputs: true
});
var multiStats = img.reduceRegion({
  reducer: reducer,
  geometry: geom,
  scale: 30,
  crs: 'EPSG:3310',
});
print('Mean & SD band values, Santa Cruz Mountains ecoregion', multiStats);

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 with SWIR1, NIR, and green bands.
img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508').select(
    ['SR_B6', 'SR_B5', 'SR_B3']
)

# Santa Cruz Mountains ecoregion geometry.
geom = (
    ee.FeatureCollection('EPA/Ecoregions/2013/L4')
    .filter('us_l4name == "Santa Cruz Mountains"')
    .geometry()
)

# Display layers on the map.
m = geemap.Map()
m.set_center(-122.08, 37.22, 9)
m.add_layer(img, {'min': 10000, 'max': 20000}, 'Landsat image')
m.add_layer(geom, {'color': 'white'}, 'Santa Cruz Mountains ecoregion')
display(m)

# Calculate median band values within Santa Cruz Mountains ecoregion. It is
# good practice to explicitly define "scale" (or "crsTransform") and "crs"
# parameters of the analysis to avoid unexpected results from undesired
# defaults when e.g. reducing a composite image.
stats = img.reduceRegion(
    reducer=ee.Reducer.median(),
    geometry=geom,
    scale=30,  # meters
    crs='EPSG:3310',  # California Albers projection
)

# A dictionary is returned keys are band names, values are the statistic.
display('Median band values, Santa Cruz Mountains ecoregion', stats)

# You can combine reducers to calculate e.g. mean and standard deviation
# simultaneously. The output dictionary keys are the concatenation of the band
# names and statistic names, separated by an underscore.
reducer = ee.Reducer.mean().combine(
    reducer2=ee.Reducer.stdDev(), sharedInputs=True
)
multi_stats = img.reduceRegion(
    reducer=reducer,
    geometry=geom,
    scale=30,
    crs='EPSG:3310',
)
display('Mean & SD band values, Santa Cruz Mountains ecoregion', multi_stats)