ImageCollection 總覽

ImageCollection 是圖片的堆疊或序列。

從集合 ID 建構

您可以將 Earth Engine 資產 ID 貼到 ImageCollection 建構函式，即可載入 ImageCollection。您可以在資料目錄中找到 ImageCollection ID。例如，如要載入 Sentinel-2 地表反射率集合：

程式碼編輯器 (JavaScript)

var sentinelCollection = ee.ImageCollection('COPERNICUS/S2_SR');

Python 設定

請參閱「 Python 環境」頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。

import ee
import geemap.core as geemap

Colab (Python)

sentinel_collection = ee.ImageCollection('COPERNICUS/S2_SR')

這個集合包含公開目錄中的每張 Sentinel-2 影像。有很多。通常您會想篩選資料集，如這裡或這裡所示。

從圖片清單建構

建構函式 ee.ImageCollection() 或便利方法 ee.ImageCollection.fromImages() 會根據圖片清單建立圖片集合。您也可以合併現有圖片集合來建立新的圖片集合。例如：

程式碼編輯器 (JavaScript)

// Create arbitrary constant images.
var constant1 = ee.Image(1);
var constant2 = ee.Image(2);

// Create a collection by giving a list to the constructor.
var collectionFromConstructor = ee.ImageCollection([constant1, constant2]);
print('collectionFromConstructor: ', collectionFromConstructor);

// Create a collection with fromImages().
var collectionFromImages = ee.ImageCollection.fromImages(
  [ee.Image(3), ee.Image(4)]);
print('collectionFromImages: ', collectionFromImages);

// Merge two collections.
var mergedCollection = collectionFromConstructor.merge(collectionFromImages);
print('mergedCollection: ', mergedCollection);

// Create a toy FeatureCollection
var features = ee.FeatureCollection(
  [ee.Feature(null, {foo: 1}), ee.Feature(null, {foo: 2})]);

// Create an ImageCollection from the FeatureCollection
// by mapping a function over the FeatureCollection.
var images = features.map(function(feature) {
  return ee.Image(ee.Number(feature.get('foo')));
});

// Print the resultant collection.
print('Image collection: ', images);

Python 設定

請參閱「 Python 環境」頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。

import ee
import geemap.core as geemap

Colab (Python)

# Create arbitrary constant images.
constant_1 = ee.Image(1)
constant_2 = ee.Image(2)

# Create a collection by giving a list to the constructor.
collection_from_constructor = ee.ImageCollection([constant_1, constant_2])
display('Collection from constructor:', collection_from_constructor)

# Create a collection with fromImages().
collection_from_images = ee.ImageCollection.fromImages(
    [ee.Image(3), ee.Image(4)]
)
display('Collection from images:', collection_from_images)

# Merge two collections.
merged_collection = collection_from_constructor.merge(collection_from_images)
display('Merged collection:', merged_collection)

# Create a toy FeatureCollection
features = ee.FeatureCollection(
    [ee.Feature(None, {'foo': 1}), ee.Feature(None, {'foo': 2})]
)

# Create an ImageCollection from the FeatureCollection
# by mapping a function over the FeatureCollection.
images = features.map(lambda feature: ee.Image(ee.Number(feature.get('foo'))))

# Display the resultant collection.
display('Image collection:', images)

請注意，在本例中，系統會透過對應函式來建立 ImageCollection，該函式會在 FeatureCollection 上傳回 Image。如要進一步瞭解對應功能，請參閱「在 ImageCollection 上進行對應」一節。如要進一步瞭解地圖項目集合，請參閱「FeatureCollection 部分」。

從 COG 清單建構

從 Cloud Storage 中的 GeoTIFF 建立 ImageCollection。例如：

程式碼編輯器 (JavaScript)

// All the GeoTiffs are in this folder.
var uriBase = 'gs://gcp-public-data-landsat/LC08/01/001/002/' +
    'LC08_L1GT_001002_20160817_20170322_01_T2/';

// List of URIs, one for each band.
var uris = ee.List([
  uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B2.TIF',
  uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B3.TIF',
  uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B4.TIF',
  uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF',
]);

// Make a collection from the list of images.
var images = uris.map(ee.Image.loadGeoTIFF);
var collection = ee.ImageCollection(images);

// Get an RGB image from the collection of bands.
var rgb = collection.toBands().rename(['B2', 'B3', 'B4', 'B5']);
Map.centerObject(rgb);
Map.addLayer(rgb, {bands: ['B4', 'B3', 'B2'], min: 0, max: 20000}, 'rgb');

Python 設定

請參閱「 Python 環境」頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。

import ee
import geemap.core as geemap

Colab (Python)

# All the GeoTiffs are in this folder.
uri_base = (
    'gs://gcp-public-data-landsat/LC08/01/001/002/'
    + 'LC08_L1GT_001002_20160817_20170322_01_T2/'
)

# List of URIs, one for each band.
uris = ee.List([
    uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B2.TIF',
    uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B3.TIF',
    uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B4.TIF',
    uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF',
])

# Make a collection from the list of images.
images = uris.map(lambda uri: ee.Image.loadGeoTIFF(uri))
collection = ee.ImageCollection(images)

# Get an RGB image from the collection of bands.
rgb = collection.toBands().rename(['B2', 'B3', 'B4', 'B5'])
m = geemap.Map()
m.center_object(rgb)
m.add_layer(rgb, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 20000}, 'rgb')
m

進一步瞭解如何從 Cloud GeoTiff 載入圖片。

從 Zarr v2 陣列建構

沿著較高維度取得圖片切片，即可從 Cloud Storage 中的 Zarr v2 陣列建立 ImageCollection。例如：

程式碼編輯器 (JavaScript)

var timeStart = 1000000;
var timeEnd = 1000048;
var zarrV2ArrayImages = ee.ImageCollection.loadZarrV2Array({
  uri:
      'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',
  proj: 'EPSG:4326',
  axis: 0,
  starts: [timeStart],
  ends: [timeEnd]
});

print(zarrV2ArrayImages);

Map.addLayer(zarrV2ArrayImages, {min: -0.0001, max: 0.00005}, 'Evaporation');

Python 設定

請參閱「 Python 環境」頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。

import ee
import geemap.core as geemap

Colab (Python)

time_start = 1000000
time_end = 1000048
zarr_v2_array_images = ee.ImageCollection.loadZarrV2Array(
    uri='gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',
    proj='EPSG:4326',
    axis=0,
    starts=[time_start],
    ends=[time_end],
)

display(zarr_v2_array_images)

m = geemap.Map()
m.add_layer(
    zarr_v2_array_images, {'min': -0.0001, 'max': 0.00005}, 'Evaporation'
)
m