Export.image.toAsset

建立批次工作，將影像匯出為點陣圖，並匯出至 Earth Engine 資產。你可以從「工作」分頁啟動工作。

用量傳回
Export.image.toAsset(image, description, assetId, pyramidingPolicy, dimensions, region, scale, crs, crsTransform, maxPixels, shardSize, priority)
引數類型詳細資料
image圖片要匯出的圖片。
description字串 (選用)使用者可解讀的任務名稱。預設值為「myExportImageTask」。
assetId字串 (選用)目的地資產 ID。
pyramidingPolicy物件 (選用)要套用至圖片中每個波段的金字塔化政策，以波段名稱做為鍵。值必須是下列其中之一：平均值、樣本、最小值、最大值或眾數。預設值為「mean」。特殊鍵「.default」可用於變更所有頻帶的預設值。
dimensionsNumber|String，選填匯出圖片時使用的尺寸。可接受單一正整數做為最大尺寸，或「WIDTHxHEIGHT」，其中 WIDTH 和 HEIGHT 皆為正整數。
regionGeometry.LinearRing|Geometry.Polygon|String，選用代表要匯出區域的 LinearRing、Polygon 或座標。這些可以是 Geometry 物件，也可以是序列化為字串的座標。
scale號碼 (選填)以每像素公尺為單位的解析度。預設值為 1000。
crs字串 (選用)用於匯出圖片的 CRS。
crsTransformList<Number>|String，選用用於匯出圖片的仿射轉換。必須定義「crs」。
maxPixels號碼 (選填)限制匯出像素數量。根據預設，如果匯出作業超過 1 億像素，系統會顯示錯誤訊息。明確設定這個值可提高或降低這個限制。
shardSize號碼 (選填)圖片計算時所用圖塊的大小 (以像素為單位)。預設值為 256。
priority號碼 (選填)專案內工作的優先順序。系統會優先排定優先順序較高的工作。必須是介於 0 至 9999 之間的整數。預設值為 100。

範例

程式碼編輯器 (JavaScript)

// A Landsat 8 surface reflectance image.
var image = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508')
  .select(['SR_B.']);  // reflectance bands

// A region of interest.
var region = ee.Geometry.BBox(-122.24, 37.13, -122.11, 37.20);

// Set the export "scale" and "crs" parameters.
Export.image.toAsset({
  image: image,
  description: 'image_export',
  assetId: 'projects/<project-name>/assets/<asset-name>',  // <> modify these
  region: region,
  scale: 30,
  crs: 'EPSG:5070'
});

// Use the "crsTransform" export parameter instead of "scale" for more control
// over the output grid. Here, "crsTransform" is set to align the output grid
// with the grid of another dataset. To view an image's CRS transform:
// print(image.projection())
Export.image.toAsset({
  image: image,
  description: 'image_export_crstransform',
  assetId: 'projects/<project-name>/assets/<asset-name>',  // <> modify these
  region: region,
  crsTransform: [30, 0, -2493045, 0, -30, 3310005],
  crs: 'EPSG:5070'
});

// If the export has more than 1e8 pixels, set "maxPixels" higher.
Export.image.toAsset({
  image: image,
  description: 'image_export_maxpixels',
  assetId: 'projects/<project-name>/assets/<asset-name>',  // <> modify these
  region: region,
  scale: 30,
  crs: 'EPSG:5070',
  maxPixels: 1e13
});

// The default "pyramidingPolicy" is mean. If data are categorical,
// consider mode.
Export.image.toAsset({
  image: image.select('SR_B5'),
  description: 'image_export_pyramiding',
  assetId: 'projects/<project-name>/assets/<asset-name>',  // <> modify these
  region: region,
  scale: 30,
  crs: 'EPSG:5070',
  pyramidingPolicy: {SR_B5: 'mode'}
});

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)

# A Landsat 8 surface reflectance image.
image = ee.Image(
    'LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508'
).select(['SR_B.'])  # reflectance bands

# A region of interest.
region = ee.Geometry.BBox(-122.24, 37.13, -122.11, 37.20)

# Set the export "scale" and "crs" parameters.
task = ee.batch.Export.image.toAsset(
    image=image,
    description='image_export',
    assetId='projects/<project-name>/assets/<asset-name>',  # <> modify these
    region=region,
    scale=30,
    crs='EPSG:5070'
)
task.start()

# Use the "crsTransform" export parameter instead of "scale" for more control
# over the output grid. Here, "crsTransform" is set to align the output grid
# with the grid of another dataset. To view an image's CRS transform:
# print(image.projection().getInfo())
task = ee.batch.Export.image.toAsset(
    image=image,
    description='image_export_crstransform',
    assetId='projects/<project-name>/assets/<asset-name>',  # <> modify these
    region=region,
    crsTransform=[30, 0, -2493045, 0, -30, 3310005],
    crs='EPSG:5070'
)
task.start()

# If the export has more than 1e8 pixels, set "maxPixels" higher.
task = ee.batch.Export.image.toAsset(
    image=image,
    description='image_export_maxpixels',
    assetId='projects/<project-name>/assets/<asset-name>',  # <> modify these
    region=region,
    scale=30,
    crs='EPSG:5070',
    maxPixels=1e13
)
task.start()

# The default "pyramidingPolicy" is mean. If data are categorical,
# consider mode.
task = ee.batch.Export.image.toAsset(
    image=image.select('SR_B5'),
    description='image_export_pyramiding',
    assetId='projects/<project-name>/assets/<asset-name>',  # <> modify these
    region=region,
    scale=30,
    crs='EPSG:5070',
    pyramidingPolicy={'SR_B5': 'mode'}
)
task.start()