ee.ImageCollection.aggregate_histogram

  • aggregate_histogram calculates a histogram of a specified property across an ImageCollection.

  • It takes the collection and the property name as inputs.

  • The output is a dictionary containing the histogram data (e.g., bucket boundaries and counts).

  • This function is useful for understanding the distribution of property values within a collection, like cloud cover across satellite images.

  • You can use the resulting histogram to visualize or analyze the frequency of different property values.

 Aggregates over a given property of the objects in a collection, calculating a histogram of the selected property.

UsageReturns
ImageCollection.aggregate_histogram(property)Dictionary
ArgumentTypeDetails
this: collectionFeatureCollectionThe collection to aggregate over.
propertyStringThe property to use from each element of the collection.

Examples

Code Editor (JavaScript)

// A Lansat 8 TOA image collection for a specific year and location.
var col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
  .filterBounds(ee.Geometry.Point([-122.073, 37.188]))
  .filterDate('2018', '2019');

// An image property of interest, percent cloud cover in this case.
var prop = 'CLOUD_COVER';

// Use ee.ImageCollection.aggregate_* functions to fetch information about
// values of a selected property across all images in the collection. For
// example, produce a list of all values, get counts, and calculate statistics.
print('List of property values', col.aggregate_array(prop));
print('Count of property values', col.aggregate_count(prop));
print('Count of distinct property values', col.aggregate_count_distinct(prop));
print('First collection element property value', col.aggregate_first(prop));
print('Histogram of property values', col.aggregate_histogram(prop));
print('Min of property values', col.aggregate_min(prop));
print('Max of property values', col.aggregate_max(prop));

// The following methods are applicable to numerical properties only.
print('Mean of property values', col.aggregate_mean(prop));
print('Sum of property values', col.aggregate_sum(prop));
print('Product of property values', col.aggregate_product(prop));
print('Std dev (sample) of property values', col.aggregate_sample_sd(prop));
print('Variance (sample) of property values', col.aggregate_sample_var(prop));
print('Std dev (total) of property values', col.aggregate_total_sd(prop));
print('Variance (total) of property values', col.aggregate_total_var(prop));
print('Summary stats of property values', col.aggregate_stats(prop));

// Note that if the property is formatted as a string, min and max will
// respectively return the first and last values according to alphanumeric
// order of the property values.
var propString = 'LANDSAT_SCENE_ID';
print('List of property values (string)', col.aggregate_array(propString));
print('Min of property values (string)', col.aggregate_min(propString));
print('Max of property values (string)', col.aggregate_max(propString));

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)

from pprint import pprint

# A Lansat 8 TOA image collection for a specific year and location.
col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA").filterBounds(
    ee.Geometry.Point([-122.073, 37.188])).filterDate('2018', '2019')

# An image property of interest, percent cloud cover in this case.
prop = 'CLOUD_COVER'

# Use ee.ImageCollection.aggregate_* functions to fetch information about
# values of a selected property across all images in the collection. For
# example, produce a list of all values, get counts, and calculate statistics.
print('List of property values:', col.aggregate_array(prop).getInfo())
print('Count of property values:', col.aggregate_count(prop).getInfo())
print('Count of distinct property values:',
      col.aggregate_count_distinct(prop).getInfo())
print('First collection element property value:',
      col.aggregate_first(prop).getInfo())
print('Histogram of property values:')
pprint(col.aggregate_histogram(prop).getInfo())
print('Min of property values:', col.aggregate_min(prop).getInfo())
print('Max of property values:', col.aggregate_max(prop).getInfo())

# The following methods are applicable to numerical properties only.
print('Mean of property values:', col.aggregate_mean(prop).getInfo())
print('Sum of property values:', col.aggregate_sum(prop).getInfo())
print('Product of property values:', col.aggregate_product(prop).getInfo())
print('Std dev (sample) of property values:',
      col.aggregate_sample_sd(prop).getInfo())
print('Variance (sample) of property values:',
      col.aggregate_sample_var(prop).getInfo())
print('Std dev (total) of property values:',
      col.aggregate_total_sd(prop).getInfo())
print('Variance (total) of property values:',
      col.aggregate_total_var(prop).getInfo())
print('Summary stats of property values:')
pprint(col.aggregate_stats(prop).getInfo())

# Note that if the property is formatted as a string, min and max will
# respectively return the first and last values according to alphanumeric
# order of the property values.
prop_string = 'LANDSAT_SCENE_ID'
print('List of property values (string):',
      col.aggregate_array(prop_string).getInfo())
print('Min of property values (string):',
      col.aggregate_min(prop_string).getInfo())
print('Max of property values (string):',
      col.aggregate_max(prop_string).getInfo())