Filtering an ImageCollection

  • Earth Engine offers various convenience methods like filterDate() and filterBounds() for filtering image collections.

  • The general-purpose imageCollection.filter() method can be used with an ee.Filter for more specific filtering.

  • Filtering by properties like CLOUD_COVER can improve the quality of image composites.

As illustrated in the Get Started section and the ImageCollection Information section, Earth Engine provides a variety of convenience methods for filtering image collections. Specifically, many common use cases are handled by imageCollection.filterDate(), and imageCollection.filterBounds(). For general purpose filtering, use imageCollection.filter() with an ee.Filter as an argument. The following example demonstrates both convenience methods and filter() to identify and remove images with high cloud cover from an ImageCollection.

Code Editor (JavaScript)

// Load Landsat 8 data, filter by date, month, and bounds.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
  .filterDate('2015-01-01', '2018-01-01')  // Three years of data
  .filter(ee.Filter.calendarRange(11, 2, 'month'))  // Only Nov-Feb observations
  .filterBounds(ee.Geometry.Point(25.8544, -18.08874));  // Intersecting ROI

// Also filter the collection by the CLOUD_COVER property.
var filtered = collection.filter(ee.Filter.eq('CLOUD_COVER', 0));

// Create two composites to check the effect of filtering by CLOUD_COVER.
var badComposite = collection.mean();
var goodComposite = filtered.mean();

// Display the composites.
Map.setCenter(25.8544, -18.08874, 13);
Map.addLayer(badComposite,
             {bands: ['B3', 'B2', 'B1'], min: 0.05, max: 0.35, gamma: 1.1},
             'Bad composite');
Map.addLayer(goodComposite,
             {bands: ['B3', 'B2', 'B1'], min: 0.05, max: 0.35, gamma: 1.1},
             'Good composite');

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 Landsat 8 data, filter by date, month, and bounds.
collection = (
    ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
    # Three years of data
    .filterDate('2015-01-01', '2018-01-01')
    # Only Nov-Feb observations
    .filter(ee.Filter.calendarRange(11, 2, 'month'))
    # Intersecting ROI
    .filterBounds(ee.Geometry.Point(25.8544, -18.08874))
)

# Also filter the collection by the CLOUD_COVER property.
filtered = collection.filter(ee.Filter.eq('CLOUD_COVER', 0))

# Create two composites to check the effect of filtering by CLOUD_COVER.
bad_composite = collection.mean()
good_composite = filtered.mean()

# Display the composites.
m = geemap.Map()
m.set_center(25.8544, -18.08874, 13)
m.add_layer(
    bad_composite,
    {'bands': ['B3', 'B2', 'B1'], 'min': 0.05, 'max': 0.35, 'gamma': 1.1},
    'Bad composite',
)
m.add_layer(
    good_composite,
    {'bands': ['B3', 'B2', 'B1'], 'min': 0.05, 'max': 0.35, 'gamma': 1.1},
    'Good composite',
)
m