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      ee.ImageCollection.aggregate_count_distinct
    
    
      
    
    
      
      Restez organisé à l'aide des collections
    
    
      
      Enregistrez et classez les contenus selon vos préférences.
    
  
  
      
    
  
  
  
  
  
    
  
  
    
    
    
  
  
Agrège une propriété donnée des objets d'une collection, en calculant le nombre de valeurs distinctes pour la propriété sélectionnée.
| Utilisation | Renvoie | 
|---|
| ImageCollection.aggregate_count_distinct(property) | Nombre | 
| Argument | Type | Détails | 
|---|
| ceci : collection | FeatureCollection | Collection à agréger. | 
| property | Chaîne | Propriété à utiliser pour chaque élément de la collection. | 
  
  
  Exemples
  
    
  
  
    
    
  
  
  
  
    
    
    
      Éditeur de code (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));
  
    
  
  
    
  
  
  
  
    
  
    
  Configuration de Python
  Consultez la page 
    Environnement Python pour en savoir plus sur l'API Python et sur l'utilisation de geemap pour le développement interactif.
  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))
  
  
  
  
  
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
  Dernière mise à jour le 2025/10/30 (UTC).
  
  
  
    
      [null,null,["Dernière mise à jour le 2025/10/30 (UTC)."],[],["The core functionality involves using `aggregate_*` functions on an `ImageCollection` to analyze a specific property. The `aggregate_count_distinct(property)` method returns the number of unique values for a given property across all images in the collection. Other functions include retrieving a list of property values, count of all values, first value, min/max, histogram, and statistical measures like mean, sum, variance, and standard deviation. The provided examples use cloud cover and scene ID properties for demonstration.\n"]]