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ee.ImageCollection.reduceToImage
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
通过对与每个像素相交的所有要素的所选属性应用归约函数,从要素集合创建图像。
用法 | 返回 |
---|
ImageCollection.reduceToImage(properties, reducer) | 图片 |
参数 | 类型 | 详细信息 |
---|
此:collection | FeatureCollection | 要与每个输出像素相交的要素集合。 |
properties | 列表 | 要从每个特征中选择并传递到 reducer 中的属性。 |
reducer | 缩减器 | 一种用于将每个相交要素的属性合并到最终结果中以存储在像素中的 Reducer。 |
示例
代码编辑器 (JavaScript)
var col = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
.filterBounds(ee.Geometry.BBox(-124.0, 43.2, -116.5, 46.3))
.filterDate('2021', '2022');
// Image visualization settings.
var visParams = {
bands: ['B4', 'B3', 'B2'],
min: 0.01,
max: 0.25
};
Map.addLayer(col.mean(), visParams, 'RGB mean');
// Reduce the geometry (footprint) of images in the collection to an image.
// Image property values are applied to the pixels intersecting each
// image's geometry and then a per-pixel reduction is performed according
// to the selected reducer. Here, the image cloud cover property is assigned
// to the pixels intersecting image geometry and then reduced to a single
// image representing the per-pixel mean image cloud cover.
var meanCloudCover = col.reduceToImage({
properties: ['CLOUD_COVER'],
reducer: ee.Reducer.mean()
});
Map.setCenter(-119.87, 44.76, 6);
Map.addLayer(meanCloudCover, {min: 0, max: 50}, 'Cloud cover mean');
Python 设置
如需了解 Python API 和如何使用 geemap
进行交互式开发,请参阅
Python 环境页面。
import ee
import geemap.core as geemap
Colab (Python)
col = (
ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
.filterBounds(ee.Geometry.BBox(-124.0, 43.2, -116.5, 46.3))
.filterDate('2021', '2022')
)
# Image visualization settings.
vis_params = {'bands': ['B4', 'B3', 'B2'], 'min': 0.01, 'max': 0.25}
m = geemap.Map()
m.add_layer(col.mean(), vis_params, 'RGB mean')
# Reduce the geometry (footprint) of images in the collection to an image.
# Image property values are applied to the pixels intersecting each
# image's geometry and then a per-pixel reduction is performed according
# to the selected reducer. Here, the image cloud cover property is assigned
# to the pixels intersecting image geometry and then reduced to a single
# image representing the per-pixel mean image cloud cover.
mean_cloud_cover = col.reduceToImage(
properties=['CLOUD_COVER'], reducer=ee.Reducer.mean()
)
m.set_center(-119.87, 44.76, 6)
m.add_layer(mean_cloud_cover, {'min': 0, 'max': 50}, 'Cloud cover mean')
m
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最后更新时间 (UTC):2025-07-26。
[null,null,["最后更新时间 (UTC):2025-07-26。"],[[["\u003cp\u003e\u003ccode\u003ereduceToImage\u003c/code\u003e transforms an image collection into a single image by applying a reducer to pixel-intersecting features.\u003c/p\u003e\n"],["\u003cp\u003eIt uses specified properties from each feature within the collection for the reduction process.\u003c/p\u003e\n"],["\u003cp\u003eUsers define a reducer (e.g., mean, median) to combine intersecting feature properties into a final pixel value in the output image.\u003c/p\u003e\n"],["\u003cp\u003eThis function is helpful for tasks like calculating mean cloud cover across a collection of satellite images, as shown in the provided example.\u003c/p\u003e\n"]]],[],null,["# ee.ImageCollection.reduceToImage\n\nCreates an image from a feature collection by applying a reducer over the selected properties of all the features that intersect each pixel.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|------------------------------------------------------|---------|\n| ImageCollection.reduceToImage`(properties, reducer)` | Image |\n\n| Argument | Type | Details |\n|--------------------|-------------------|-------------------------------------------------------------------------------------------------------------|\n| this: `collection` | FeatureCollection | Feature collection to intersect with each output pixel. |\n| `properties` | List | Properties to select from each feature and pass into the reducer. |\n| `reducer` | Reducer | A Reducer to combine the properties of each intersecting feature into a final result to store in the pixel. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\nvar col = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')\n .filterBounds(ee.Geometry.BBox(-124.0, 43.2, -116.5, 46.3))\n .filterDate('2021', '2022');\n\n// Image visualization settings.\nvar visParams = {\n bands: ['B4', 'B3', 'B2'],\n min: 0.01,\n max: 0.25\n};\nMap.addLayer(col.mean(), visParams, 'RGB mean');\n\n// Reduce the geometry (footprint) of images in the collection to an image.\n// Image property values are applied to the pixels intersecting each\n// image's geometry and then a per-pixel reduction is performed according\n// to the selected reducer. Here, the image cloud cover property is assigned\n// to the pixels intersecting image geometry and then reduced to a single\n// image representing the per-pixel mean image cloud cover.\nvar meanCloudCover = col.reduceToImage({\n properties: ['CLOUD_COVER'],\n reducer: ee.Reducer.mean()\n});\n\nMap.setCenter(-119.87, 44.76, 6);\nMap.addLayer(meanCloudCover, {min: 0, max: 50}, 'Cloud cover mean');\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\n### Colab (Python)\n\n```python\ncol = (\n ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')\n .filterBounds(ee.Geometry.BBox(-124.0, 43.2, -116.5, 46.3))\n .filterDate('2021', '2022')\n)\n\n# Image visualization settings.\nvis_params = {'bands': ['B4', 'B3', 'B2'], 'min': 0.01, 'max': 0.25}\nm = geemap.Map()\nm.add_layer(col.mean(), vis_params, 'RGB mean')\n\n# Reduce the geometry (footprint) of images in the collection to an image.\n# Image property values are applied to the pixels intersecting each\n# image's geometry and then a per-pixel reduction is performed according\n# to the selected reducer. Here, the image cloud cover property is assigned\n# to the pixels intersecting image geometry and then reduced to a single\n# image representing the per-pixel mean image cloud cover.\nmean_cloud_cover = col.reduceToImage(\n properties=['CLOUD_COVER'], reducer=ee.Reducer.mean()\n)\n\nm.set_center(-119.87, 44.76, 6)\nm.add_layer(mean_cloud_cover, {'min': 0, 'max': 50}, 'Cloud cover mean')\nm\n```"]]