ImageCollection Information and Metadata

  • This guide demonstrates how to programmatically retrieve information from Earth Engine ImageCollections, such as size, date range, image properties, and specific images.

  • You can print an ImageCollection to the console, but for collections larger than 5000 images, filtering is necessary before printing to avoid slowdowns.

  • Examples are provided using the JavaScript and Python APIs for tasks like filtering, sorting, getting statistics, and limiting the collection size.

  • It shows how to get specific images from the collection, including the least cloudy or the most recent ones, based on properties and sorting.

  • The code snippets are readily usable in Code Editor, Colab, or any Python environment set up for Earth Engine, with geemap suggested for interactive Python development.

As with Images, there are a variety of ways to get information about an ImageCollection. The collection can be printed directly to the console, but the console printout is limited to 5000 elements. Collections larger than 5000 images will need to be filtered before printing. Printing a large collection will be correspondingly slower. The following example shows various ways of getting information about image collections programmatically:

Code Editor (JavaScript)

// Load a Landsat 8 ImageCollection 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('2014-03-01', '2014-08-01');
print('Collection: ', collection);

// Get the number of images.
var count = collection.size();
print('Count: ', count);

// Get the date range of images in the collection.
var range = collection.reduceColumns(ee.Reducer.minMax(), ['system:time_start'])
print('Date range: ', ee.Date(range.get('min')), ee.Date(range.get('max')))

// Get statistics for a property of the images in the collection.
var sunStats = collection.aggregate_stats('SUN_ELEVATION');
print('Sun elevation statistics: ', sunStats);

// Sort by a cloud cover property, get the least cloudy image.
var image = ee.Image(collection.sort('CLOUD_COVER').first());
print('Least cloudy image: ', image);

// Limit the collection to the 10 most recent images.
var recent = collection.sort('system:time_start', false).limit(10);
print('Recent images: ', recent);

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)

# Load a Landsat 8 ImageCollection 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('2014-03-01', '2014-08-01')
)
display('Collection:', collection)

# Get the number of images.
count = collection.size()
display('Count:', count)

# Get the date range of images in the collection.
range = collection.reduceColumns(ee.Reducer.minMax(), ['system:time_start'])
display('Date range:', ee.Date(range.get('min')), ee.Date(range.get('max')))

# Get statistics for a property of the images in the collection.
sun_stats = collection.aggregate_stats('SUN_ELEVATION')
display('Sun elevation statistics:', sun_stats)

# Sort by a cloud cover property, get the least cloudy image.
image = ee.Image(collection.sort('CLOUD_COVER').first())
display('Least cloudy image:', image)

# Limit the collection to the 10 most recent images.
recent = collection.sort('system:time_start', False).limit(10)
display('Recent images:', recent)