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:

// Load a Landsat 8 collection for a single path-row.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/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.
var median = collection.median();
Map.setCenter(-122.3578, 37.7726, 12);
Map.addLayer(median, {bands: ['B4', 'B3', 'B2'], max: 0.3}, 'Median');

import ee
import geemap.core as geemap

# Load a Landsat 8 collection for a single path-row.
collection = (
    ee.ImageCollection('LANDSAT/LC08/C02/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()
m = geemap.Map()
m.set_center(-122.3578, 37.7726, 12)
m.add_layer(median, {'bands': ['B4', 'B3', 'B2'], 'max': 0.3}, 'Median')
m

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:

// Reduce the collection with a median reducer.
var 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');

import ee
import geemap.core as geemap

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

# Display the median image.
m.add_layer(
    median,
    {'bands': ['B4_median', 'B3_median', 'B2_median'], 'max': 0.3},
    'Also median',
)
m

Note that the band names differ as a result of using reduce() instead of the convenience method. Specifically, the names of the reducer have been appended to the band names.

More complex reductions are also possible using reduce(). For example, to compute the long term linear trend over a collection, use one of the linear regression reducers. The following code computes the linear trend of MODIS Enhanced Vegetation Index (EVI):

// This function adds a band representing the image timestamp.
var addTime = function(image) {
  return image.addBands(image.metadata('system:time_start')
    // Convert milliseconds from epoch to years to aid in
    // interpretation of the following trend calculation.
    .divide(1000 * 60 * 60 * 24 * 365));
};

// Load a MODIS collection, filter to several years of 16 day mosaics,
// and map the time band function over it.
var collection = ee.ImageCollection('MODIS/006/MYD13A1')
  .filterDate('2004-01-01', '2010-10-31')
  .map(addTime);

// Select the bands to model with the independent variable first.
var trend = collection.select(['system:time_start', 'EVI'])
  // Compute the linear trend over time.
  .reduce(ee.Reducer.linearFit());

// Display the trend with increasing slopes in green, decreasing in red.
Map.setCenter(-96.943, 39.436, 5);
Map.addLayer(
    trend,
    {min: 0, max: [-100, 100, 10000], bands: ['scale', 'scale', 'offset']},
    'EVI trend');

import ee
import geemap.core as geemap

# This function adds a band representing the image timestamp.
def add_time(image):
  return image.addBands(
      image.metadata('system:time_start')
      # Convert milliseconds from epoch to years to aid in
      # interpretation of the following trend calculation.
      .divide(1000 * 60 * 60 * 24 * 365)
  )


# Load a MODIS collection, filter to several years of 16 day mosaics,
# and map the time band function over it.
collection = (
    ee.ImageCollection('MODIS/006/MYD13A1')
    .filterDate('2004-01-01', '2010-10-31')
    .map(add_time)
)

# Select the bands to model with the independent variable first.
trend = collection.select(['system:time_start', 'EVI']).reduce(
    # Compute the linear trend over time.
    ee.Reducer.linearFit()
)

# Display the trend with increasing slopes in green, decreasing in red.
m.set_center(-96.943, 39.436, 5)
m = geemap.Map()
m.add_layer(
    trend,
    {
        'min': 0,
        'max': [-100, 100, 10000],
        'bands': ['scale', 'scale', 'offset'],
    },
    'EVI trend',
)
m

Note that the output of the reduction in this example is a two banded image with one band for the slope of a linear regression (scale) and one band for the intercept (offset). Explore the API documentation to see a list of the reducers that are available to reduce an ImageCollection to a single Image.

Composites have no projection

Composite images created by reducing an image collection are able to produce pixels in any requested projection and therefore have no fixed output projection. Instead, composites have the default projection of WGS-84 with 1-degree resolution pixels. Composites with the default projection will be computed in whatever output projection is requested. A request occurs by displaying the composite in the Code Editor (learn about how the Code editor sets scale and projection), or by explicitly specifying a projection/scale as in an aggregation such as ReduceRegion or Export.