Reducing image collection

Reducing an ImageCollection

To composite images in an ImageCollection, use imageCollection.reduce(). This will composite all the images in the collection to a single image representing, for example, the min, max, mean or standard deviation of the images. (See the Reducers section for more information about reducers). For example, to create a median value image from a collection:

Create an interactive map¶

In [1]:

import ee
import geemap

import ee import geemap

In [2]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

Out[2]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Compute a median image¶

In [3]:

# Load a Landsat 8 collection for a single path-row.
collection = (
    ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
    .filter(ee.Filter.eq('WRS_PATH', 44))
    .filter(ee.Filter.eq('WRS_ROW', 34))
    .filterDate('2014-01-01', '2015-01-01')
)

# Compute a median image and display.
median = collection.median()
Map.setCenter(-122.3578, 37.7726, 12)
Map.addLayer(median, {'bands': ['B4', 'B3', 'B2'], 'max': 0.3}, 'median')

# Load a Landsat 8 collection for a single path-row. collection = ( ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') .filter(ee.Filter.eq('WRS_PATH', 44)) .filter(ee.Filter.eq('WRS_ROW', 34)) .filterDate('2014-01-01', '2015-01-01') ) # Compute a median image and display. median = collection.median() Map.setCenter(-122.3578, 37.7726, 12) Map.addLayer(median, {'bands': ['B4', 'B3', 'B2'], 'max': 0.3}, 'median')

In [4]:

collection.size().getInfo()

collection.size().getInfo()

Out[4]:

19

In [5]:

collection.aggregate_array("system:id").getInfo()

collection.aggregate_array("system:id").getInfo()

Out[5]:

['LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140113',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140129',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140214',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140403',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140419',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140505',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140521',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140606',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140622',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140708',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140724',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140809',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140825',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140910',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140926',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20141012',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20141028',
 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20141231']

Use median reducer¶

At each location in the output image, in each band, the pixel value is the median of all unmasked pixels in the input imagery (the images in the collection). In the previous example, median() is a convenience method for the following call:

In [6]:

# Reduce the collection with a median reducer.
median = collection.reduce(ee.Reducer.median())

# Display the median image.
Map.addLayer(
    median,
    {'bands': ['B4_median', 'B3_median', 'B2_median'], 'max': 0.3},
    'also median',
)
Map

# Reduce the collection with a median reducer. median = collection.reduce(ee.Reducer.median()) # Display the median image. Map.addLayer( median, {'bands': ['B4_median', 'B3_median', 'B2_median'], 'max': 0.3}, 'also median', ) Map

Out[6]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Create an image composite¶

In [7]:

states = ee.FeatureCollection('TIGER/2018/States')
Map.addLayer(states, {}, "US States")

states = ee.FeatureCollection('TIGER/2018/States') Map.addLayer(states, {}, "US States")

In [8]:

ca = states.filter(ee.Filter.eq("NAME", "California"))
Map.addLayer(ca, {}, "California")

ca = states.filter(ee.Filter.eq("NAME", "California")) Map.addLayer(ca, {}, "California")

In [9]:

collection = (
    ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
    .filterBounds(ca)
    .filterDate('2020-01-01', '2021-01-01')
)

collection = ( ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') .filterBounds(ca) .filterDate('2020-01-01', '2021-01-01') )

In [10]:

collection.size().getInfo()

collection.size().getInfo()

Out[10]:

838

In [11]:

image = collection.median().clip(ca)
Map.addLayer(image, {'bands': ['B4', 'B3', 'B2'], 'max': 0.3}, 'Landsat 2020')

image = collection.median().clip(ca) Map.addLayer(image, {'bands': ['B4', 'B3', 'B2'], 'max': 0.3}, 'Landsat 2020')

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Last update: 2022-03-25