ee.ImageCollection.aggregate_min
קל לארגן דפים בעזרת אוספים
אפשר לשמור ולסווג תוכן על סמך ההעדפות שלך.
מקבץ נתונים לפי מאפיין נתון של האובייקטים באוסף, ומחשב את הערך המינימלי של הערכים של המאפיין שנבחר.
שימוש | החזרות |
---|
ImageCollection.aggregate_min(property) | |
ארגומנט | סוג | פרטים |
---|
זה: collection | FeatureCollection | האוסף שרוצים לצבור על סמך נתונים שלו. |
property | מחרוזת | המאפיין שבו צריך להשתמש מכל רכיב באוסף. |
דוגמאות
Code Editor (JavaScript)
// A Lansat 8 TOA image collection for a specific year and location.
var col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
.filterBounds(ee.Geometry.Point([-122.073, 37.188]))
.filterDate('2018', '2019');
// An image property of interest, percent cloud cover in this case.
var prop = 'CLOUD_COVER';
// Use ee.ImageCollection.aggregate_* functions to fetch information about
// values of a selected property across all images in the collection. For
// example, produce a list of all values, get counts, and calculate statistics.
print('List of property values', col.aggregate_array(prop));
print('Count of property values', col.aggregate_count(prop));
print('Count of distinct property values', col.aggregate_count_distinct(prop));
print('First collection element property value', col.aggregate_first(prop));
print('Histogram of property values', col.aggregate_histogram(prop));
print('Min of property values', col.aggregate_min(prop));
print('Max of property values', col.aggregate_max(prop));
// The following methods are applicable to numerical properties only.
print('Mean of property values', col.aggregate_mean(prop));
print('Sum of property values', col.aggregate_sum(prop));
print('Product of property values', col.aggregate_product(prop));
print('Std dev (sample) of property values', col.aggregate_sample_sd(prop));
print('Variance (sample) of property values', col.aggregate_sample_var(prop));
print('Std dev (total) of property values', col.aggregate_total_sd(prop));
print('Variance (total) of property values', col.aggregate_total_var(prop));
print('Summary stats of property values', col.aggregate_stats(prop));
// Note that if the property is formatted as a string, min and max will
// respectively return the first and last values according to alphanumeric
// order of the property values.
var propString = 'LANDSAT_SCENE_ID';
print('List of property values (string)', col.aggregate_array(propString));
print('Min of property values (string)', col.aggregate_min(propString));
print('Max of property values (string)', col.aggregate_max(propString));
הגדרת Python
בדף
סביבת Python מפורט מידע על Python API ועל השימוש ב-geemap
לפיתוח אינטראקטיבי.
import ee
import geemap.core as geemap
Colab (Python)
from pprint import pprint
# A Lansat 8 TOA image collection for a specific year and location.
col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA").filterBounds(
ee.Geometry.Point([-122.073, 37.188])).filterDate('2018', '2019')
# An image property of interest, percent cloud cover in this case.
prop = 'CLOUD_COVER'
# Use ee.ImageCollection.aggregate_* functions to fetch information about
# values of a selected property across all images in the collection. For
# example, produce a list of all values, get counts, and calculate statistics.
print('List of property values:', col.aggregate_array(prop).getInfo())
print('Count of property values:', col.aggregate_count(prop).getInfo())
print('Count of distinct property values:',
col.aggregate_count_distinct(prop).getInfo())
print('First collection element property value:',
col.aggregate_first(prop).getInfo())
print('Histogram of property values:')
pprint(col.aggregate_histogram(prop).getInfo())
print('Min of property values:', col.aggregate_min(prop).getInfo())
print('Max of property values:', col.aggregate_max(prop).getInfo())
# The following methods are applicable to numerical properties only.
print('Mean of property values:', col.aggregate_mean(prop).getInfo())
print('Sum of property values:', col.aggregate_sum(prop).getInfo())
print('Product of property values:', col.aggregate_product(prop).getInfo())
print('Std dev (sample) of property values:',
col.aggregate_sample_sd(prop).getInfo())
print('Variance (sample) of property values:',
col.aggregate_sample_var(prop).getInfo())
print('Std dev (total) of property values:',
col.aggregate_total_sd(prop).getInfo())
print('Variance (total) of property values:',
col.aggregate_total_var(prop).getInfo())
print('Summary stats of property values:')
pprint(col.aggregate_stats(prop).getInfo())
# Note that if the property is formatted as a string, min and max will
# respectively return the first and last values according to alphanumeric
# order of the property values.
prop_string = 'LANDSAT_SCENE_ID'
print('List of property values (string):',
col.aggregate_array(prop_string).getInfo())
print('Min of property values (string):',
col.aggregate_min(prop_string).getInfo())
print('Max of property values (string):',
col.aggregate_max(prop_string).getInfo())
אלא אם צוין אחרת, התוכן של דף זה הוא ברישיון Creative Commons Attribution 4.0 ודוגמאות הקוד הן ברישיון Apache 2.0. לפרטים, ניתן לעיין במדיניות האתר Google Developers. Java הוא סימן מסחרי רשום של חברת Oracle ו/או של השותפים העצמאיים שלה.
עדכון אחרון: 2025-07-25 (שעון UTC).
[null,null,["עדכון אחרון: 2025-07-25 (שעון UTC)."],[[["\u003cp\u003e\u003ccode\u003eaggregate_min\u003c/code\u003e calculates the minimum value of a specified property across an ImageCollection.\u003c/p\u003e\n"],["\u003cp\u003eIt is applicable to both numerical and string properties within the collection.\u003c/p\u003e\n"],["\u003cp\u003eFor numerical properties, it returns the lowest value.\u003c/p\u003e\n"],["\u003cp\u003eFor string properties, it returns the first value based on alphanumeric order.\u003c/p\u003e\n"],["\u003cp\u003eThis function is helpful for identifying the minimum value of a property, such as cloud cover or a scene ID, across a collection of images.\u003c/p\u003e\n"]]],["The content describes how to use `aggregate_min` to find the minimum value of a specified property within a collection. It demonstrates fetching information about a property across all elements, including counts, statistics, and histograms. Additional aggregation functions for calculating the mean, sum, product, and variance of numerical properties are included. For string properties, `aggregate_min` returns the first value based on alphanumeric order. Example codes are provided in JavaScript and Python.\n"],null,["# ee.ImageCollection.aggregate_min\n\nAggregates over a given property of the objects in a collection, calculating the minimum of the values of the selected property.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|-------------------------------------------|---------|\n| ImageCollection.aggregate_min`(property)` | |\n\n| Argument | Type | Details |\n|--------------------|-------------------|----------------------------------------------------------|\n| this: `collection` | FeatureCollection | The collection to aggregate over. |\n| `property` | String | The property to use from each element of the collection. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// A Lansat 8 TOA image collection for a specific year and location.\nvar col = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_TOA\")\n .filterBounds(ee.Geometry.Point([-122.073, 37.188]))\n .filterDate('2018', '2019');\n\n// An image property of interest, percent cloud cover in this case.\nvar prop = 'CLOUD_COVER';\n\n// Use ee.ImageCollection.aggregate_* functions to fetch information about\n// values of a selected property across all images in the collection. For\n// example, produce a list of all values, get counts, and calculate statistics.\nprint('List of property values', col.aggregate_array(prop));\nprint('Count of property values', col.aggregate_count(prop));\nprint('Count of distinct property values', col.aggregate_count_distinct(prop));\nprint('First collection element property value', col.aggregate_first(prop));\nprint('Histogram of property values', col.aggregate_histogram(prop));\nprint('Min of property values', col.aggregate_min(prop));\nprint('Max of property values', col.aggregate_max(prop));\n\n// The following methods are applicable to numerical properties only.\nprint('Mean of property values', col.aggregate_mean(prop));\nprint('Sum of property values', col.aggregate_sum(prop));\nprint('Product of property values', col.aggregate_product(prop));\nprint('Std dev (sample) of property values', col.aggregate_sample_sd(prop));\nprint('Variance (sample) of property values', col.aggregate_sample_var(prop));\nprint('Std dev (total) of property values', col.aggregate_total_sd(prop));\nprint('Variance (total) of property values', col.aggregate_total_var(prop));\nprint('Summary stats of property values', col.aggregate_stats(prop));\n\n// Note that if the property is formatted as a string, min and max will\n// respectively return the first and last values according to alphanumeric\n// order of the property values.\nvar propString = 'LANDSAT_SCENE_ID';\nprint('List of property values (string)', col.aggregate_array(propString));\nprint('Min of property values (string)', col.aggregate_min(propString));\nprint('Max of property values (string)', col.aggregate_max(propString));\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\nfrom pprint import pprint\n\n# A Lansat 8 TOA image collection for a specific year and location.\ncol = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_TOA\").filterBounds(\n ee.Geometry.Point([-122.073, 37.188])).filterDate('2018', '2019')\n\n# An image property of interest, percent cloud cover in this case.\nprop = 'CLOUD_COVER'\n\n# Use ee.ImageCollection.aggregate_* functions to fetch information about\n# values of a selected property across all images in the collection. For\n# example, produce a list of all values, get counts, and calculate statistics.\nprint('List of property values:', col.aggregate_array(prop).getInfo())\nprint('Count of property values:', col.aggregate_count(prop).getInfo())\nprint('Count of distinct property values:',\n col.aggregate_count_distinct(prop).getInfo())\nprint('First collection element property value:',\n col.aggregate_first(prop).getInfo())\nprint('Histogram of property values:')\npprint(col.aggregate_histogram(prop).getInfo())\nprint('Min of property values:', col.aggregate_min(prop).getInfo())\nprint('Max of property values:', col.aggregate_max(prop).getInfo())\n\n# The following methods are applicable to numerical properties only.\nprint('Mean of property values:', col.aggregate_mean(prop).getInfo())\nprint('Sum of property values:', col.aggregate_sum(prop).getInfo())\nprint('Product of property values:', col.aggregate_product(prop).getInfo())\nprint('Std dev (sample) of property values:',\n col.aggregate_sample_sd(prop).getInfo())\nprint('Variance (sample) of property values:',\n col.aggregate_sample_var(prop).getInfo())\nprint('Std dev (total) of property values:',\n col.aggregate_total_sd(prop).getInfo())\nprint('Variance (total) of property values:',\n col.aggregate_total_var(prop).getInfo())\nprint('Summary stats of property values:')\npprint(col.aggregate_stats(prop).getInfo())\n\n# Note that if the property is formatted as a string, min and max will\n# respectively return the first and last values according to alphanumeric\n# order of the property values.\nprop_string = 'LANDSAT_SCENE_ID'\nprint('List of property values (string):',\n col.aggregate_array(prop_string).getInfo())\nprint('Min of property values (string):',\n col.aggregate_min(prop_string).getInfo())\nprint('Max of property values (string):',\n col.aggregate_max(prop_string).getInfo())\n```"]]