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ee.ImageCollection.count
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。
通过计算所有匹配波段堆栈中每个像素处具有有效掩码的图片数量,来缩减图片集合。系统会按名称匹配频段。
用法 | 返回 |
---|
ImageCollection.count() | 图片 |
参数 | 类型 | 详细信息 |
---|
此:collection | ImageCollection | 要缩减的图片集合。 |
示例
代码编辑器 (JavaScript)
// Sentinel-2 image collection for July 2021 intersecting a point of interest.
// Reflectance, cloud probability, and scene classification bands are selected.
var col = ee.ImageCollection('COPERNICUS/S2_SR')
.filterDate('2021-07-01', '2021-08-01')
.filterBounds(ee.Geometry.Point(-122.373, 37.448))
.select('B.*|MSK_CLDPRB|SCL');
// Visualization parameters for reflectance RGB.
var visRefl = {
bands: ['B11', 'B8', 'B3'],
min: 0,
max: 4000
};
Map.setCenter(-122.373, 37.448, 9);
Map.addLayer(col, visRefl, 'Collection reference', false);
// Reduce the collection to a single image using a variety of methods.
var mean = col.mean();
Map.addLayer(mean, visRefl, 'Mean (B11, B8, B3)');
var median = col.median();
Map.addLayer(median, visRefl, 'Median (B11, B8, B3)');
var min = col.min();
Map.addLayer(min, visRefl, 'Min (B11, B8, B3)');
var max = col.max();
Map.addLayer(max, visRefl, 'Max (B11, B8, B3)');
var sum = col.sum();
Map.addLayer(sum,
{bands: ['MSK_CLDPRB'], min: 0, max: 500}, 'Sum (MSK_CLDPRB)');
var product = col.product();
Map.addLayer(product,
{bands: ['MSK_CLDPRB'], min: 0, max: 1e10}, 'Product (MSK_CLDPRB)');
// ee.ImageCollection.mode returns the most common value. If multiple mode
// values occur, the minimum mode value is returned.
var mode = col.mode();
Map.addLayer(mode, {bands: ['SCL'], min: 1, max: 11}, 'Mode (pixel class)');
// ee.ImageCollection.count returns the frequency of valid observations. Here,
// image pixels are masked based on cloud probability to add valid observation
// variability to the collection. Note that pixels with no valid observations
// are masked out of the returned image.
var notCloudCol = col.map(function(img) {
return img.updateMask(img.select('MSK_CLDPRB').lte(10));
});
var count = notCloudCol.count();
Map.addLayer(count, {min: 1, max: 5}, 'Count (not cloud observations)');
// ee.ImageCollection.mosaic composites images according to their position in
// the collection (priority is last to first) and pixel mask status, where
// invalid (mask value 0) pixels are filled by preceding valid (mask value >0)
// pixels.
var mosaic = notCloudCol.mosaic();
Map.addLayer(mosaic, visRefl, 'Mosaic (B11, B8, B3)');
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)
# Sentinel-2 image collection for July 2021 intersecting a point of interest.
# Reflectance, cloud probability, and scene classification bands are selected.
col = (
ee.ImageCollection('COPERNICUS/S2_SR')
.filterDate('2021-07-01', '2021-08-01')
.filterBounds(ee.Geometry.Point(-122.373, 37.448))
.select('B.*|MSK_CLDPRB|SCL')
)
# Visualization parameters for reflectance RGB.
vis_refl = {'bands': ['B11', 'B8', 'B3'], 'min': 0, 'max': 4000}
m = geemap.Map()
m.set_center(-122.373, 37.448, 9)
m.add_layer(col, vis_refl, 'Collection reference', False)
# Reduce the collection to a single image using a variety of methods.
mean = col.mean()
m.add_layer(mean, vis_refl, 'Mean (B11, B8, B3)')
median = col.median()
m.add_layer(median, vis_refl, 'Median (B11, B8, B3)')
min = col.min()
m.add_layer(min, vis_refl, 'Min (B11, B8, B3)')
max = col.max()
m.add_layer(max, vis_refl, 'Max (B11, B8, B3)')
sum = col.sum()
m.add_layer(
sum, {'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 500}, 'Sum (MSK_CLDPRB)'
)
product = col.product()
m.add_layer(
product,
{'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 1e10},
'Product (MSK_CLDPRB)',
)
# ee.ImageCollection.mode returns the most common value. If multiple mode
# values occur, the minimum mode value is returned.
mode = col.mode()
m.add_layer(
mode, {'bands': ['SCL'], 'min': 1, 'max': 11}, 'Mode (pixel class)'
)
# ee.ImageCollection.count returns the frequency of valid observations. Here,
# image pixels are masked based on cloud probability to add valid observation
# variability to the collection. Note that pixels with no valid observations
# are masked out of the returned image.
not_cloud_col = col.map(
lambda img: img.updateMask(img.select('MSK_CLDPRB').lte(10))
)
count = not_cloud_col.count()
m.add_layer(count, {'min': 1, 'max': 5}, 'Count (not cloud observations)')
# ee.ImageCollection.mosaic composites images according to their position in
# the collection (priority is last to first) and pixel mask status, where
# invalid (mask value 0) pixels are filled by preceding valid (mask value >0)
# pixels.
mosaic = not_cloud_col.mosaic()
m.add_layer(mosaic, vis_refl, 'Mosaic (B11, B8, B3)')
m
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
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\u003eImageCollection.count()\u003c/code\u003e reduces an image collection by calculating the number of images with valid data at each pixel location.\u003c/p\u003e\n"],["\u003cp\u003eBands with the same name are matched across images within the collection for the calculation.\u003c/p\u003e\n"],["\u003cp\u003ePixels with no valid observations are masked out in the resulting image.\u003c/p\u003e\n"],["\u003cp\u003eThe output is a single image where pixel values represent the count of valid observations.\u003c/p\u003e\n"]]],["The `ImageCollection.count()` method reduces an image collection into a single image by counting the number of images with a valid mask at each pixel, across all matching bands. Bands are matched by name. It returns an image where each pixel's value represents the frequency of valid observations at that location. An example in the provided code uses this method to count cloud-free observations. The collection can be reduced by a variety of methods.\n"],null,["# ee.ImageCollection.count\n\nReduces an image collection by calculating the number of images with a valid mask at each pixel across the stack of all matching bands. Bands are matched by name.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|---------------------------|---------|\n| ImageCollection.count`()` | Image |\n\n| Argument | Type | Details |\n|--------------------|-----------------|---------------------------------|\n| this: `collection` | ImageCollection | The image collection to reduce. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// Sentinel-2 image collection for July 2021 intersecting a point of interest.\n// Reflectance, cloud probability, and scene classification bands are selected.\nvar col = ee.ImageCollection('COPERNICUS/S2_SR')\n .filterDate('2021-07-01', '2021-08-01')\n .filterBounds(ee.Geometry.Point(-122.373, 37.448))\n .select('B.*|MSK_CLDPRB|SCL');\n\n// Visualization parameters for reflectance RGB.\nvar visRefl = {\n bands: ['B11', 'B8', 'B3'],\n min: 0,\n max: 4000\n};\nMap.setCenter(-122.373, 37.448, 9);\nMap.addLayer(col, visRefl, 'Collection reference', false);\n\n// Reduce the collection to a single image using a variety of methods.\nvar mean = col.mean();\nMap.addLayer(mean, visRefl, 'Mean (B11, B8, B3)');\n\nvar median = col.median();\nMap.addLayer(median, visRefl, 'Median (B11, B8, B3)');\n\nvar min = col.min();\nMap.addLayer(min, visRefl, 'Min (B11, B8, B3)');\n\nvar max = col.max();\nMap.addLayer(max, visRefl, 'Max (B11, B8, B3)');\n\nvar sum = col.sum();\nMap.addLayer(sum,\n {bands: ['MSK_CLDPRB'], min: 0, max: 500}, 'Sum (MSK_CLDPRB)');\n\nvar product = col.product();\nMap.addLayer(product,\n {bands: ['MSK_CLDPRB'], min: 0, max: 1e10}, 'Product (MSK_CLDPRB)');\n\n// ee.ImageCollection.mode returns the most common value. If multiple mode\n// values occur, the minimum mode value is returned.\nvar mode = col.mode();\nMap.addLayer(mode, {bands: ['SCL'], min: 1, max: 11}, 'Mode (pixel class)');\n\n// ee.ImageCollection.count returns the frequency of valid observations. Here,\n// image pixels are masked based on cloud probability to add valid observation\n// variability to the collection. Note that pixels with no valid observations\n// are masked out of the returned image.\nvar notCloudCol = col.map(function(img) {\n return img.updateMask(img.select('MSK_CLDPRB').lte(10));\n});\nvar count = notCloudCol.count();\nMap.addLayer(count, {min: 1, max: 5}, 'Count (not cloud observations)');\n\n// ee.ImageCollection.mosaic composites images according to their position in\n// the collection (priority is last to first) and pixel mask status, where\n// invalid (mask value 0) pixels are filled by preceding valid (mask value \u003e0)\n// pixels.\nvar mosaic = notCloudCol.mosaic();\nMap.addLayer(mosaic, visRefl, 'Mosaic (B11, B8, B3)');\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\n# Sentinel-2 image collection for July 2021 intersecting a point of interest.\n# Reflectance, cloud probability, and scene classification bands are selected.\ncol = (\n ee.ImageCollection('COPERNICUS/S2_SR')\n .filterDate('2021-07-01', '2021-08-01')\n .filterBounds(ee.Geometry.Point(-122.373, 37.448))\n .select('B.*|MSK_CLDPRB|SCL')\n)\n\n# Visualization parameters for reflectance RGB.\nvis_refl = {'bands': ['B11', 'B8', 'B3'], 'min': 0, 'max': 4000}\nm = geemap.Map()\nm.set_center(-122.373, 37.448, 9)\nm.add_layer(col, vis_refl, 'Collection reference', False)\n\n# Reduce the collection to a single image using a variety of methods.\nmean = col.mean()\nm.add_layer(mean, vis_refl, 'Mean (B11, B8, B3)')\n\nmedian = col.median()\nm.add_layer(median, vis_refl, 'Median (B11, B8, B3)')\n\nmin = col.min()\nm.add_layer(min, vis_refl, 'Min (B11, B8, B3)')\n\nmax = col.max()\nm.add_layer(max, vis_refl, 'Max (B11, B8, B3)')\n\nsum = col.sum()\nm.add_layer(\n sum, {'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 500}, 'Sum (MSK_CLDPRB)'\n)\n\nproduct = col.product()\nm.add_layer(\n product,\n {'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 1e10},\n 'Product (MSK_CLDPRB)',\n)\n\n# ee.ImageCollection.mode returns the most common value. If multiple mode\n# values occur, the minimum mode value is returned.\nmode = col.mode()\nm.add_layer(\n mode, {'bands': ['SCL'], 'min': 1, 'max': 11}, 'Mode (pixel class)'\n)\n\n# ee.ImageCollection.count returns the frequency of valid observations. Here,\n# image pixels are masked based on cloud probability to add valid observation\n# variability to the collection. Note that pixels with no valid observations\n# are masked out of the returned image.\nnot_cloud_col = col.map(\n lambda img: img.updateMask(img.select('MSK_CLDPRB').lte(10))\n)\ncount = not_cloud_col.count()\nm.add_layer(count, {'min': 1, 'max': 5}, 'Count (not cloud observations)')\n\n# ee.ImageCollection.mosaic composites images according to their position in\n# the collection (priority is last to first) and pixel mask status, where\n# invalid (mask value 0) pixels are filled by preceding valid (mask value \u003e0)\n# pixels.\nmosaic = not_cloud_col.mosaic()\nm.add_layer(mosaic, vis_refl, 'Mosaic (B11, B8, B3)')\nm\n```"]]