Bildübersicht
Mit Sammlungen den Überblick behalten
Sie können Inhalte basierend auf Ihren Einstellungen speichern und kategorisieren.
Wie im Dokument Erste Schritte erwähnt, werden Rasterdaten in Earth Engine als Image-Objekte dargestellt. Bilder bestehen aus einem oder mehreren Bändern. Jedes Band hat einen eigenen Namen, Datentyp, Maßstab, Maske und Projektion. Jedes Bild hat Metadaten, die als eine Reihe von Attributen gespeichert sind.
ee.Image-Konstruktor
Bilder können geladen werden, indem Sie eine Earth Engine-Asset-ID in den ee.Image-Konstruktor einfügen. Bild-IDs finden Sie im Data Catalog.
Zum Beispiel für ein digitales Höhenmodell (NASADEM):
Code-Editor (JavaScript)
var loadedImage = ee.Image('NASA/NASADEM_HGT/001');
Python einrichten
Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite
Python-Umgebung.
import ee
import geemap.core as geemap
Colab (Python)
loaded_image = ee.Image('NASA/NASADEM_HGT/001')
Sie können auch das Suchtool im Code-Editor verwenden. Wenn Sie das Asset importieren, wird der Code für die Bildkonstruktion für Sie im Imports-Bereich des Code-Editors geschrieben. Sie können auch eine persönliche Asset-ID als Argument für den ee.Image-Konstruktor verwenden.
ee.Image von einem ee.ImageCollection erhalten
Die Standardmethode zum Abrufen eines Bildes aus einer Sammlung besteht darin, die Sammlung mit Filtern in absteigender Reihenfolge der Spezifität zu filtern. Um beispielsweise ein Bild aus der Sentinel-2-Sammlung für Oberflächenreflexion abzurufen:
Code-Editor (JavaScript)
var first = ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(ee.Geometry.Point(-70.48, 43.3631))
.filterDate('2019-01-01', '2019-12-31')
.sort('CLOUDY_PIXEL_PERCENTAGE')
.first();
Map.centerObject(first, 11);
Map.addLayer(first, {bands: ['B4', 'B3', 'B2'], min: 0, max: 2000}, 'first');
Python einrichten
Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite
Python-Umgebung.
import ee
import geemap.core as geemap
Colab (Python)
first = (
ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(ee.Geometry.Point(-70.48, 43.3631))
.filterDate('2019-01-01', '2019-12-31')
.sort('CLOUDY_PIXEL_PERCENTAGE')
.first()
)
# Define a map centered on southern Maine.
m = geemap.Map(center=[43.7516, -70.8155], zoom=11)
# Add the image layer to the map and display it.
m.add_layer(
first, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 2000}, 'first'
)
display(m)
Die Sortierung erfolgt nach den Filtern. Vermeiden Sie es, die gesamte Sammlung zu sortieren.
Bilder aus Cloud-GeoTIFFs
Mit ee.Image.loadGeoTIFF() können Sie Bilder aus Cloud-optimierten GeoTIFFs in Google Cloud Storage laden.
Das in Google Cloud gehostete öffentliche Landsat-Dataset enthält beispielsweise dieses GeoTIFF, das dem Band 5 einer Landsat 8-Szene entspricht. Sie können dieses Bild mit ee.Image.loadGeoTIFF() aus Cloud Storage laden:
Code-Editor (JavaScript)
var uri = 'gs://gcp-public-data-landsat/LC08/01/001/002/' +
'LC08_L1GT_001002_20160817_20170322_01_T2/' +
'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF';
var cloudImage = ee.Image.loadGeoTIFF(uri);
print(cloudImage);
Python einrichten
Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite
Python-Umgebung.
import ee
import geemap.core as geemap
Colab (Python)
uri = (
'gs://gcp-public-data-landsat/LC08/01/001/002/'
+ 'LC08_L1GT_001002_20160817_20170322_01_T2/'
+ 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF'
)
cloud_image = ee.Image.loadGeoTIFF(uri)
display(cloud_image)
Wenn Sie ein Cloud-optimiertes GeoTIFF neu laden möchten, das Sie aus Earth Engine in Cloud Storage exportieren, müssen Sie beim Exportieren cloudOptimized auf true setzen, wie hier beschrieben.
Bilder aus Zarr v2-Arrays
Mit ee.Image.loadZarrV2Array() können Sie ein Bild aus einem Zarr v2-Array in Google Cloud Storage laden. Das öffentliche ERA5-Dataset, das in Google Cloud gehostet wird, enthält beispielsweise dieses Zarr v2-Array, das den Metern an Wasser entspricht, die von der Erdoberfläche verdunstet sind. Sie können dieses Array mit ee.Image.loadZarrV2Array() aus Cloud Storage laden:
Code-Editor (JavaScript)
var timeStart = 1000000;
var timeEnd = 1000010;
var zarrV2ArrayImage = ee.Image.loadZarrV2Array({
uri:
'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',
proj: 'EPSG:4326',
starts: [timeStart],
ends: [timeEnd]
});
print(zarrV2ArrayImage);
Map.addLayer(zarrV2ArrayImage, {min: -0.0001, max: 0.00005}, 'Evaporation');
Python einrichten
Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite
Python-Umgebung.
import ee
import geemap.core as geemap
Colab (Python)
time_start = 1000000
time_end = 1000010
zarr_v2_array_image = ee.Image.loadZarrV2Array(
uri='gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',
proj='EPSG:4326',
starts=[time_start],
ends=[time_end],
)
display(zarr_v2_array_image)
m.add_layer(
zarr_v2_array_image, {'min': -0.0001, 'max': 0.00005}, 'Evaporation'
)
m
Konstante Bilder
Sie können Bilder nicht nur anhand der ID laden, sondern auch aus Konstanten, Listen oder anderen geeigneten Earth Engine-Objekten erstellen. Im Folgenden werden Methoden zum Erstellen von Bildern, zum Abrufen von Band-Teilmengen und zum Bearbeiten von Bändern veranschaulicht:
Code-Editor (JavaScript)
// Create a constant image.
var image1 = ee.Image(1);
print(image1);
// Concatenate two images into one multi-band image.
var image2 = ee.Image(2);
var image3 = ee.Image.cat([image1, image2]);
print(image3);
// Create a multi-band image from a list of constants.
var multiband = ee.Image([1, 2, 3]);
print(multiband);
// Select and (optionally) rename bands.
var renamed = multiband.select(
['constant', 'constant_1', 'constant_2'], // old names
['band1', 'band2', 'band3'] // new names
);
print(renamed);
// Add bands to an image.
var image4 = image3.addBands(ee.Image(42));
print(image4);
Python einrichten
Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite
Python-Umgebung.
import ee
import geemap.core as geemap
Colab (Python)
# Create a constant image.
image_1 = ee.Image(1)
display(image_1)
# Concatenate two images into one multi-band image.
image_2 = ee.Image(2)
image_3 = ee.Image.cat([image_1, image_2])
display(image_3)
# Create a multi-band image from a list of constants.
multiband = ee.Image([1, 2, 3])
display(multiband)
# Select and (optionally) rename bands.
renamed = multiband.select(
['constant', 'constant_1', 'constant_2'], # old names
['band1', 'band2', 'band3'], # new names
)
display(renamed)
# Add bands to an image.
image_4 = image_3.addBands(ee.Image(42))
display(image_4)
Sofern nicht anders angegeben, sind die Inhalte dieser Seite unter der Creative Commons Attribution 4.0 License und Codebeispiele unter der Apache 2.0 License lizenziert. Weitere Informationen finden Sie in den Websiterichtlinien von Google Developers. Java ist eine eingetragene Marke von Oracle und/oder seinen Partnern.
Zuletzt aktualisiert: 2025-09-03 (UTC).
[null,null,["Zuletzt aktualisiert: 2025-09-03 (UTC)."],[[["\u003cp\u003eIn Earth Engine, raster data is represented as \u003ccode\u003eImage\u003c/code\u003e objects, which can be created by loading existing assets or by defining them with constant values.\u003c/p\u003e\n"],["\u003cp\u003e\u003ccode\u003eImage\u003c/code\u003e objects can be created from Earth Engine assets, \u003ccode\u003eImageCollection\u003c/code\u003e objects, and Cloud Optimized GeoTIFFs (COG) stored in Google Cloud Storage.\u003c/p\u003e\n"],["\u003cp\u003eImages in Earth Engine are composed of bands, each with its own data type, scale, mask, and projection, and images can be manipulated using methods such as \u003ccode\u003eselect\u003c/code\u003e, \u003ccode\u003eaddBands\u003c/code\u003e, and \u003ccode\u003ecat\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003e\u003ccode\u003eImageCollection\u003c/code\u003e objects can be filtered and sorted to retrieve specific images, and \u003ccode\u003eee.Image.loadGeoTIFF()\u003c/code\u003e is used to load images from Cloud Optimized GeoTIFFs in Cloud Storage.\u003c/p\u003e\n"],["\u003cp\u003eConstant images can be created from numerical values, lists of values, and other suitable Earth Engine objects, allowing for flexible image manipulation and analysis.\u003c/p\u003e\n"]]],[],null,["|-------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|\n| [Run in Google Colab](https://colab.research.google.com/github/google/earthengine-community/blob/master/guides/linked/generated/image_overview.ipynb) | [View source on GitHub](https://github.com/google/earthengine-community/blob/master/guides/linked/generated/image_overview.ipynb) |\n\nAs mentioned in the [Get Started](/earth-engine/guides/getstarted#earth-engine-data-structures)\ndoc, raster data are represented as `Image` objects in Earth Engine. Images are\ncomposed of one or more bands and each band has its own name, data type, scale, mask\nand projection. Each image has metadata stored as a set of properties.\n\n`ee.Image` constructor\n\nImages can be loaded by pasting an Earth Engine asset ID into the `ee.Image`\nconstructor. You can find image IDs in the [data catalog](/earth-engine/datasets).\nFor example, to a digital elevation model ([NASADEM](/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001)):\n\nCode Editor (JavaScript) \n\n```javascript\nvar loadedImage = ee.Image('NASA/NASADEM_HGT/001');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\nColab (Python) \n\n```python\nloaded_image = ee.Image('NASA/NASADEM_HGT/001')\n```\n\n\nNote that finding an image through\n[the Code Editor search tool](/earth-engine/guides/playground#search-tool)\nis equivalent. When you import the asset, the image construction code is written\nfor you in the [imports section of the\nCode Editor](/earth-engine/guides/playground#imports). You can also use a personal\n[asset ID](/earth-engine/guides/manage_assets#asset_id) as the argument to the\n`ee.Image` constructor.\n\nGet an `ee.Image` from an `ee.ImageCollection`\n\n\nThe standard way to get an image out of a collection is to filter the collection, with\nfilters in order of decreasing specificity. For example, to get an image out of the\n[Sentinel-2 surface reflectance collection](/earth-engine/datasets/catalog/COPERNICUS_S2_SR):\n\nCode Editor (JavaScript) \n\n```javascript\nvar first = ee.ImageCollection('COPERNICUS/S2_SR')\n .filterBounds(ee.Geometry.Point(-70.48, 43.3631))\n .filterDate('2019-01-01', '2019-12-31')\n .sort('CLOUDY_PIXEL_PERCENTAGE')\n .first();\nMap.centerObject(first, 11);\nMap.addLayer(first, {bands: ['B4', 'B3', 'B2'], min: 0, max: 2000}, 'first');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\nColab (Python) \n\n```python\nfirst = (\n ee.ImageCollection('COPERNICUS/S2_SR')\n .filterBounds(ee.Geometry.Point(-70.48, 43.3631))\n .filterDate('2019-01-01', '2019-12-31')\n .sort('CLOUDY_PIXEL_PERCENTAGE')\n .first()\n)\n\n# Define a map centered on southern Maine.\nm = geemap.Map(center=[43.7516, -70.8155], zoom=11)\n\n# Add the image layer to the map and display it.\nm.add_layer(\n first, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 2000}, 'first'\n)\ndisplay(m)\n```\n\n\nNote that the sort is *after* the filters. Avoid sorting the entire collection.\n\nImages from Cloud GeoTIFFs\n\n\nYou can use `ee.Image.loadGeoTIFF()` to load images from\n[Cloud Optimized\nGeoTIFFs](https://github.com/cogeotiff/cog-spec/blob/master/spec.md) in [Google Cloud Storage](https://cloud.google.com/storage).\nFor example, the\n[public\nLandsat dataset](https://console.cloud.google.com/marketplace/details/usgs-public-data/landast) hosted in Google Cloud contains\n[this\nGeoTIFF](https://console.cloud.google.com/storage/browser/_details/gcp-public-data-landsat/LC08/01/001/002/LC08_L1GT_001002_20160817_20170322_01_T2/LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF), corresponding to band 5 from a Landsat 8 scene. You can load this image from\nCloud Storage using `ee.Image.loadGeoTIFF()`:\n\nCode Editor (JavaScript) \n\n```javascript\nvar uri = 'gs://gcp-public-data-landsat/LC08/01/001/002/' +\n 'LC08_L1GT_001002_20160817_20170322_01_T2/' +\n 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF';\nvar cloudImage = ee.Image.loadGeoTIFF(uri);\nprint(cloudImage);\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\nColab (Python) \n\n```python\nuri = (\n 'gs://gcp-public-data-landsat/LC08/01/001/002/'\n + 'LC08_L1GT_001002_20160817_20170322_01_T2/'\n + 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF'\n)\ncloud_image = ee.Image.loadGeoTIFF(uri)\ndisplay(cloud_image)\n```\n\n\nNote that if you want to reload a Cloud Optimized GeoTIFF that you\n[export from Earth Engine to\nCloud Storage](/earth-engine/guides/exporting#to-cloud-storage), when you do the export, set\n`cloudOptimized` to **true** as\ndescribed [here](/earth-engine/guides/exporting#configuration-parameters).\n\nImages from Zarr v2 arrays\n\n\nYou can use `ee.Image.loadZarrV2Array()` to load an image from a\n[Zarr v2 array](https://zarr-specs.readthedocs.io/en/latest/v2/v2.0.html) in\n[Google Cloud Storage](https://cloud.google.com/storage). For example, the public\nERA5 dataset hosted in Google Cloud contains\n[this Zarr v2 array](https://console.cloud.google.com/storage/browser/_details/gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray),\ncorresponding to meters of water that has evaporated from the Earth's surface. You can load\nthis array from Cloud Storage using `ee.Image.loadZarrV2Array()`:\n\nCode Editor (JavaScript) \n\n```javascript\nvar timeStart = 1000000;\nvar timeEnd = 1000010;\nvar zarrV2ArrayImage = ee.Image.loadZarrV2Array({\n uri:\n 'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',\n proj: 'EPSG:4326',\n starts: [timeStart],\n ends: [timeEnd]\n});\nprint(zarrV2ArrayImage);\nMap.addLayer(zarrV2ArrayImage, {min: -0.0001, max: 0.00005}, 'Evaporation');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\nColab (Python) \n\n```python\ntime_start = 1000000\ntime_end = 1000010\nzarr_v2_array_image = ee.Image.loadZarrV2Array(\n uri='gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',\n proj='EPSG:4326',\n starts=[time_start],\n ends=[time_end],\n)\n\ndisplay(zarr_v2_array_image)\n\nm.add_layer(\n zarr_v2_array_image, {'min': -0.0001, 'max': 0.00005}, 'Evaporation'\n)\nm\n```\n\nConstant images\n\nIn addition to loading images by ID, you can also create images\nfrom constants, lists or other suitable Earth Engine objects. The following illustrates\nmethods for creating images, getting band subsets, and manipulating bands:\n\nCode Editor (JavaScript) \n\n```javascript\n// Create a constant image.\nvar image1 = ee.Image(1);\nprint(image1);\n\n// Concatenate two images into one multi-band image.\nvar image2 = ee.Image(2);\nvar image3 = ee.Image.cat([image1, image2]);\nprint(image3);\n\n// Create a multi-band image from a list of constants.\nvar multiband = ee.Image([1, 2, 3]);\nprint(multiband);\n\n// Select and (optionally) rename bands.\nvar renamed = multiband.select(\n ['constant', 'constant_1', 'constant_2'], // old names\n ['band1', 'band2', 'band3'] // new names\n);\nprint(renamed);\n\n// Add bands to an image.\nvar image4 = image3.addBands(ee.Image(42));\nprint(image4);\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\nColab (Python) \n\n```python\n# Create a constant image.\nimage_1 = ee.Image(1)\ndisplay(image_1)\n\n# Concatenate two images into one multi-band image.\nimage_2 = ee.Image(2)\nimage_3 = ee.Image.cat([image_1, image_2])\ndisplay(image_3)\n\n# Create a multi-band image from a list of constants.\nmultiband = ee.Image([1, 2, 3])\ndisplay(multiband)\n\n# Select and (optionally) rename bands.\nrenamed = multiband.select(\n ['constant', 'constant_1', 'constant_2'], # old names\n ['band1', 'band2', 'band3'], # new names\n)\ndisplay(renamed)\n\n# Add bands to an image.\nimage_4 = image_3.addBands(ee.Image(42))\ndisplay(image_4)\n```"]]
In Google Colab ausführen
Quelle auf GitHub ansehen