ee.ImageCollection.mosaic

تدمج جميع الصور في مجموعة باستخدام القناع.

الاستخدام	المرتجعات
ImageCollection.mosaic()	صورة

الوسيطة	النوع	التفاصيل
هذا: collection	ImageCollection	المجموعة التي سيتم تحويلها إلى فسيفساء

أمثلة

محرّر الرموز البرمجية (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)');

إعداد Python

راجِع صفحة بيئة Python للحصول على معلومات حول واجهة برمجة التطبيقات Python واستخدام geemap للتطوير التفاعلي.

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