AI-generated Key Takeaways
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aggregate_array
compiles a list of all values for a specified property within an ImageCollection. -
This function is applied to an ImageCollection and requires the property name as input.
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It returns a list of all the values of the specified property across all images in the collection.
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Useful for analyzing the distribution and range of property values within a collection.
Usage | Returns |
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ImageCollection.aggregate_array(property) | List |
Argument | Type | Details |
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this: collection | FeatureCollection | The collection to aggregate over. |
property | String | The 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));
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())