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遍历 ImageCollection
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。
虽然 map() 会对集合中的每张图片应用函数，但该函数会独立访问集合中的每张图片。例如，假设您要计算时间序列在时间 t 时的累积异常值 (At)。如需获取以 At = f(Imaget, At-1) 的形式递归定义的系列，映射不起作用，因为函数 (f) 取决于上一个结果 (At-1)。例如，假设您要计算一系列相对于基准的累积常态化差值植生指标 (NDVI) 异常图像。设 A0 = 0 且 f(Imaget, At-1) = Imaget + At-1，其中 At-1 是截至时间 t-1 的累计异常，Imaget 是时间 t 的异常。使用 imageCollection.iterate() 创建此递归定义的 ImageCollection。在以下示例中，accumulate() 函数接受两个参数：集合中的图片，以及所有之前输出的列表。每次调用 iterate() 时，异常都会被添加到累计和中，结果会添加到列表中。最终结果会传递给 ImageCollection 构造函数，以获取新的图片序列：
Code Editor (JavaScript)
// Load MODIS EVI imagery.
var collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI');
// Define reference conditions from the first 10 years of data.
var reference = collection.filterDate('2001-01-01', '2010-12-31')
// Sort chronologically in descending order.
.sort('system:time_start', false);
// Compute the mean of the first 10 years.
var mean = reference.mean();
// Compute anomalies by subtracting the 2001-2010 mean from each image in a
// collection of 2011-2014 images. Copy the date metadata over to the
// computed anomaly images in the new collection.
var series = collection.filterDate('2011-01-01', '2014-12-31').map(function(image) {
return image.subtract(mean).set('system:time_start', image.get('system:time_start'));
});
// Display cumulative anomalies.
Map.setCenter(-100.811, 40.2, 5);
Map.addLayer(series.sum(),
{min: -60000, max: 60000, palette: ['FF0000', '000000', '00FF00']}, 'EVI anomaly');
// Get the timestamp from the most recent image in the reference collection.
var time0 = reference.first().get('system:time_start');
// Use imageCollection.iterate() to make a collection of cumulative anomaly over time.
// The initial value for iterate() is a list of anomaly images already processed.
// The first anomaly image in the list is just 0, with the time0 timestamp.
var first = ee.List([
// Rename the first band 'EVI'.
ee.Image(0).set('system:time_start', time0).select([0], ['EVI'])
]);
// This is a function to pass to Iterate().
// As anomaly images are computed, add them to the list.
var accumulate = function(image, list) {
// Get the latest cumulative anomaly image from the end of the list with
// get(-1). Since the type of the list argument to the function is unknown,
// it needs to be cast to a List. Since the return type of get() is unknown,
// cast it to Image.
var previous = ee.Image(ee.List(list).get(-1));
// Add the current anomaly to make a new cumulative anomaly image.
var added = image.add(previous)
// Propagate metadata to the new image.
.set('system:time_start', image.get('system:time_start'));
// Return the list with the cumulative anomaly inserted.
return ee.List(list).add(added);
};
// Create an ImageCollection of cumulative anomaly images by iterating.
// Since the return type of iterate is unknown, it needs to be cast to a List.
var cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)));
// Predefine the chart titles.
var title = {
title: 'Cumulative EVI anomaly over time',
hAxis: {title: 'Time'},
vAxis: {title: 'Cumulative EVI anomaly'},
};
// Chart some interesting locations.
var pt1 = ee.Geometry.Point(-65.544, -4.894);
print('Amazon rainforest:',
ui.Chart.image.series(
cumulative, pt1, ee.Reducer.first(), 500).setOptions(title));
var pt2 = ee.Geometry.Point(116.4647, 40.1054);
print('Beijing urbanization:',
ui.Chart.image.series(
cumulative, pt2, ee.Reducer.first(), 500).setOptions(title));
var pt3 = ee.Geometry.Point(-110.3412, 34.1982);
print('Arizona forest disturbance and recovery:',
ui.Chart.image.series(
cumulative, pt3, ee.Reducer.first(), 500).setOptions(title));
Python 设置
如需了解 Python API 以及如何使用 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)
import altair as alt
# Load MODIS EVI imagery.
collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI')
# Define reference conditions from the first 10 years of data.
reference = collection.filterDate('2001-01-01', '2010-12-31').sort(
# Sort chronologically in descending order.
'system:time_start',
False,
)
# Compute the mean of the first 10 years.
mean = reference.mean()
# Compute anomalies by subtracting the 2001-2010 mean from each image in a
# collection of 2011-2014 images. Copy the date metadata over to the
# computed anomaly images in the new collection.
series = collection.filterDate('2011-01-01', '2014-12-31').map(
lambda image: image.subtract(mean).set(
'system:time_start', image.get('system:time_start')
)
)
# Display cumulative anomalies.
m = geemap.Map()
m.set_center(-100.811, 40.2, 5)
m.add_layer(
series.sum(),
{'min': -60000, 'max': 60000, 'palette': ['FF0000', '000000', '00FF00']},
'EVI anomaly',
)
display(m)
# Get the timestamp from the most recent image in the reference collection.
time_0 = reference.first().get('system:time_start')
# Use imageCollection.iterate() to make a collection of cumulative anomaly over time.
# The initial value for iterate() is a list of anomaly images already processed.
# The first anomaly image in the list is just 0, with the time_0 timestamp.
first = ee.List([
# Rename the first band 'EVI'.
ee.Image(0)
.set('system:time_start', time_0)
.select([0], ['EVI'])
])
# This is a function to pass to Iterate().
# As anomaly images are computed, add them to the list.
def accumulate(image, list):
# Get the latest cumulative anomaly image from the end of the list with
# get(-1). Since the type of the list argument to the function is unknown,
# it needs to be cast to a List. Since the return type of get() is unknown,
# cast it to Image.
previous = ee.Image(ee.List(list).get(-1))
# Add the current anomaly to make a new cumulative anomaly image.
added = image.add(previous).set(
# Propagate metadata to the new image.
'system:time_start',
image.get('system:time_start'),
)
# Return the list with the cumulative anomaly inserted.
return ee.List(list).add(added)
# Create an ImageCollection of cumulative anomaly images by iterating.
# Since the return type of iterate is unknown, it needs to be cast to a List.
cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)))
# Predefine the chart titles.
title = 'Cumulative EVI anomaly over time'
# Chart some interesting locations.
def display_chart(region, collection):
reduced = (
collection.filterBounds(region)
.sort('system:time_start')
.map(
lambda image: ee.Feature(
None,
image.reduceRegion(ee.Reducer.first(), region, 500).set(
'time', image.get('system:time_start')
),
)
)
)
reduced_dataframe = ee.data.computeFeatures(
{'expression': reduced, 'fileFormat': 'PANDAS_DATAFRAME'}
)
alt.Chart(reduced_dataframe).mark_line().encode(
alt.X('time:T').title('Time'),
alt.Y('EVI:Q').title('Cumulative EVI anomaly'),
).properties(title=title).display()
pt_1 = ee.Geometry.Point(-65.544, -4.894)
display('Amazon rainforest:')
display_chart(pt_1, cumulative)
pt_2 = ee.Geometry.Point(116.4647, 40.1054)
display('Beijing urbanization:')
display_chart(pt_2, cumulative)
pt_3 = ee.Geometry.Point(-110.3412, 34.1982)
display('Arizona forest disturbance and recovery:')
display_chart(pt_3, cumulative)
绘制这些序列的图表可指明 NDVI 相对于之前的干扰是否趋于稳定，或者 NDVI 是否呈现向新状态发展的趋势。如需详细了解 Earth Engine 中的图表，请参阅“图表”部分。
迭代函数可以执行的操作受到限制。具体而言，它无法修改函数外的变量；无法输出任何内容；无法使用 JavaScript“if”或“for”语句。您要收集的任何结果或要保留到下一次迭代的中间信息都必须包含在函数的返回值中。您可以使用 `ee.Algorithms.If()` 执行条件运算。
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)：2025-07-25。
[null,null,["最后更新时间 (UTC)：2025-07-25。"],[[["\u003cp\u003e\u003ccode\u003eimageCollection.iterate()\u003c/code\u003e is used to perform calculations that depend on previous results within an ImageCollection, unlike \u003ccode\u003emap()\u003c/code\u003e which processes each image independently.\u003c/p\u003e\n"],["\u003cp\u003eThis example demonstrates using \u003ccode\u003eiterate()\u003c/code\u003e to calculate cumulative anomalies of the Normalized Difference Vegetation Index (NDVI) over time.\u003c/p\u003e\n"],["\u003cp\u003eThe function passed to \u003ccode\u003eiterate()\u003c/code\u003e accumulates anomalies, adding the current image's anomaly to the running sum from previous iterations.\u003c/p\u003e\n"],["\u003cp\u003eResulting cumulative anomaly ImageCollection can be charted to analyze NDVI trends and disturbances over time in different locations.\u003c/p\u003e\n"],["\u003cp\u003eThe iterated function has limitations: it can't modify external variables, print, or use JavaScript 'if'/'for' statements, requiring results and intermediate information to be within its return value.\u003c/p\u003e\n"]]],["`imageCollection.iterate()` computes cumulative anomalies, unlike `map()`, which processes images independently. It defines a series *A~t~* = *f(Image~t~, A~t-1~)*, using a function to add the current anomaly to the previous cumulative anomaly. An example shows calculating cumulative Normalized Difference Vegetation Index (NDVI) anomalies, where `accumulate()` adds the current anomaly to a running sum. The result is stored in a list and then converted to a new `ImageCollection`. The function used in iterate cannot modify variables outside itself or use `if` or `for` statements.\n"],null,["# Iterating over an ImageCollection\n\nAlthough `map()` applies a function to every image in a collection, the\nfunction visits every image in the collection independently. For example, suppose you\nwant to compute a cumulative anomaly (*A~t~* ) at time *t* from a time\nseries. To obtain a recursively defined series of the form *A~t~ =\nf(Image~t~, A~t-1~)* , mapping won't work because the function\n(*f* ) depends on the previous result (*A~t-1~* ). For example, suppose\nyou want to compute a series of cumulative Normalized Difference Vegetation Index (NDVI)\nanomaly images relative to a baseline. Let *A~0~* = 0 and\n*f(Image~t~, A~t-1~)* = *Image~t~ + A~t-1~*\nwhere *A~t-1~* is the cumulative anomaly up to time *t-1* and\n*Image~t~* is the anomaly at time *t* . Use\n`imageCollection.iterate()` to make this recursively defined\n`ImageCollection`. In the following example, the function\n`accumulate()` takes two parameters: an image in the collection, and a list\nof all the previous outputs. With each call to `iterate()`, the anomaly is\nadded to the running sum and the result is added to the list. The final result is\npassed to the `ImageCollection` constructor to get a new sequence of images:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load MODIS EVI imagery.\nvar collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI');\n\n// Define reference conditions from the first 10 years of data.\nvar reference = collection.filterDate('2001-01-01', '2010-12-31')\n // Sort chronologically in descending order.\n .sort('system:time_start', false);\n\n// Compute the mean of the first 10 years.\nvar mean = reference.mean();\n\n// Compute anomalies by subtracting the 2001-2010 mean from each image in a\n// collection of 2011-2014 images. Copy the date metadata over to the\n// computed anomaly images in the new collection.\nvar series = collection.filterDate('2011-01-01', '2014-12-31').map(function(image) {\n return image.subtract(mean).set('system:time_start', image.get('system:time_start'));\n});\n\n// Display cumulative anomalies.\nMap.setCenter(-100.811, 40.2, 5);\nMap.addLayer(series.sum(),\n {min: -60000, max: 60000, palette: ['FF0000', '000000', '00FF00']}, 'EVI anomaly');\n\n// Get the timestamp from the most recent image in the reference collection.\nvar time0 = reference.first().get('system:time_start');\n\n// Use imageCollection.iterate() to make a collection of cumulative anomaly over time.\n// The initial value for iterate() is a list of anomaly images already processed.\n// The first anomaly image in the list is just 0, with the time0 timestamp.\nvar first = ee.List([\n // Rename the first band 'EVI'.\n ee.Image(0).set('system:time_start', time0).select([0], ['EVI'])\n]);\n\n// This is a function to pass to Iterate().\n// As anomaly images are computed, add them to the list.\nvar accumulate = function(image, list) {\n // Get the latest cumulative anomaly image from the end of the list with\n // get(-1). Since the type of the list argument to the function is unknown,\n // it needs to be cast to a List. Since the return type of get() is unknown,\n // cast it to Image.\n var previous = ee.Image(ee.List(list).get(-1));\n // Add the current anomaly to make a new cumulative anomaly image.\n var added = image.add(previous)\n // Propagate metadata to the new image.\n .set('system:time_start', image.get('system:time_start'));\n // Return the list with the cumulative anomaly inserted.\n return ee.List(list).add(added);\n};\n\n// Create an ImageCollection of cumulative anomaly images by iterating.\n// Since the return type of iterate is unknown, it needs to be cast to a List.\nvar cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)));\n\n// Predefine the chart titles.\nvar title = {\n title: 'Cumulative EVI anomaly over time',\n hAxis: {title: 'Time'},\n vAxis: {title: 'Cumulative EVI anomaly'},\n};\n\n// Chart some interesting locations.\nvar pt1 = ee.Geometry.Point(-65.544, -4.894);\nprint('Amazon rainforest:',\n ui.Chart.image.series(\n cumulative, pt1, ee.Reducer.first(), 500).setOptions(title));\n\nvar pt2 = ee.Geometry.Point(116.4647, 40.1054);\nprint('Beijing urbanization:',\n ui.Chart.image.series(\n cumulative, pt2, ee.Reducer.first(), 500).setOptions(title));\n\nvar pt3 = ee.Geometry.Point(-110.3412, 34.1982);\nprint('Arizona forest disturbance and recovery:',\n ui.Chart.image.series(\n cumulative, pt3, ee.Reducer.first(), 500).setOptions(title));\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\nimport altair as alt\n# Load MODIS EVI imagery.\ncollection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI')\n\n# Define reference conditions from the first 10 years of data.\nreference = collection.filterDate('2001-01-01', '2010-12-31').sort(\n # Sort chronologically in descending order.\n 'system:time_start',\n False,\n)\n\n# Compute the mean of the first 10 years.\nmean = reference.mean()\n\n# Compute anomalies by subtracting the 2001-2010 mean from each image in a\n# collection of 2011-2014 images. Copy the date metadata over to the\n# computed anomaly images in the new collection.\nseries = collection.filterDate('2011-01-01', '2014-12-31').map(\n lambda image: image.subtract(mean).set(\n 'system:time_start', image.get('system:time_start')\n )\n)\n\n# Display cumulative anomalies.\nm = geemap.Map()\nm.set_center(-100.811, 40.2, 5)\nm.add_layer(\n series.sum(),\n {'min': -60000, 'max': 60000, 'palette': ['FF0000', '000000', '00FF00']},\n 'EVI anomaly',\n)\ndisplay(m)\n\n# Get the timestamp from the most recent image in the reference collection.\ntime_0 = reference.first().get('system:time_start')\n\n# Use imageCollection.iterate() to make a collection of cumulative anomaly over time.\n# The initial value for iterate() is a list of anomaly images already processed.\n# The first anomaly image in the list is just 0, with the time_0 timestamp.\nfirst = ee.List([\n # Rename the first band 'EVI'.\n ee.Image(0)\n .set('system:time_start', time_0)\n .select([0], ['EVI'])\n])\n\n# This is a function to pass to Iterate().\n# As anomaly images are computed, add them to the list.\ndef accumulate(image, list):\n # Get the latest cumulative anomaly image from the end of the list with\n # get(-1). Since the type of the list argument to the function is unknown,\n # it needs to be cast to a List. Since the return type of get() is unknown,\n # cast it to Image.\n previous = ee.Image(ee.List(list).get(-1))\n # Add the current anomaly to make a new cumulative anomaly image.\n added = image.add(previous).set(\n # Propagate metadata to the new image.\n 'system:time_start',\n image.get('system:time_start'),\n )\n # Return the list with the cumulative anomaly inserted.\n return ee.List(list).add(added)\n\n# Create an ImageCollection of cumulative anomaly images by iterating.\n# Since the return type of iterate is unknown, it needs to be cast to a List.\ncumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)))\n\n# Predefine the chart titles.\ntitle = 'Cumulative EVI anomaly over time'\n\n# Chart some interesting locations.\ndef display_chart(region, collection):\n reduced = (\n collection.filterBounds(region)\n .sort('system:time_start')\n .map(\n lambda image: ee.Feature(\n None,\n image.reduceRegion(ee.Reducer.first(), region, 500).set(\n 'time', image.get('system:time_start')\n ),\n )\n )\n )\n reduced_dataframe = ee.data.computeFeatures(\n {'expression': reduced, 'fileFormat': 'PANDAS_DATAFRAME'}\n )\n alt.Chart(reduced_dataframe).mark_line().encode(\n alt.X('time:T').title('Time'),\n alt.Y('EVI:Q').title('Cumulative EVI anomaly'),\n ).properties(title=title).display()\n\npt_1 = ee.Geometry.Point(-65.544, -4.894)\ndisplay('Amazon rainforest:')\ndisplay_chart(pt_1, cumulative)\n\npt_2 = ee.Geometry.Point(116.4647, 40.1054)\ndisplay('Beijing urbanization:')\ndisplay_chart(pt_2, cumulative)\n\npt_3 = ee.Geometry.Point(-110.3412, 34.1982)\ndisplay('Arizona forest disturbance and recovery:')\ndisplay_chart(pt_3, cumulative)\n```\n\nCharting these sequences indicates whether NDVI is stabilizing relative to previous\ndisturbances or whether NDVI is trending to a new state. Learn more about charts in\nEarth Engine from the [Charts section](/earth-engine/guides/charts).\n\nThe iterated function is limited in the operations it can perform. Specifically, it can't\nmodify variables outside the function; it can't print anything; it can't use JavaScript 'if'\nor 'for' statements. Any results you wish to collect or intermediate information you wish to\ncarry over to the next iteration must be in the function's return value. You can use\n\\`ee.Algorithms.If()\\` to perform conditional operations."]]
