ee.ImageCollection.aggregate_sample_var

  • The aggregate_sample_var function calculates the sample variance of a specified property across all objects in a collection.

  • This function is applied to an ImageCollection and takes the property name as a string argument.

  • The function returns a single Number representing the calculated sample variance.

  • The examples demonstrate how to use aggregate_sample_var with a numerical property like 'CLOUD_COVER' in both JavaScript and Python.

 Aggregates over a given property of the objects in a collection, calculating the sample variance of the values of the selected property.

UsageReturns
ImageCollection.aggregate_sample_var(property)Number
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)

# 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.
display('List of property values:', col.aggregate_array(prop))
display('Count of property values:', col.aggregate_count(prop))
display('Count of distinct property values:',
        col.aggregate_count_distinct(prop))
display('First collection element property value:', col.aggregate_first(prop))
display('Histogram of property values:', col.aggregate_histogram(prop))
display('Min of property values:', col.aggregate_min(prop))
display('Max of property values:', col.aggregate_max(prop))

# The following methods are applicable to numerical properties only.
display('Mean of property values:', col.aggregate_mean(prop))
display('Sum of property values:', col.aggregate_sum(prop))
display('Product of property values:', col.aggregate_product(prop))
display('Std dev (sample) of property values:', col.aggregate_sample_sd(prop))
display('Variance (sample) of property values:', col.aggregate_sample_var(prop))
display('Std dev (total) of property values', col.aggregate_total_sd(prop))
display('Variance (total) of property values:', col.aggregate_total_var(prop))
display('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.
prop_string = 'LANDSAT_SCENE_ID'
display('List of property values (string):', col.aggregate_array(prop_string))
display('Min of property values (string):', col.aggregate_min(prop_string))
display('Max of property values (string):', col.aggregate_max(prop_string))