合成和马赛克

一般来说，合成是指根据聚合函数将空间重叠的图片合并为一张图片的过程。拼接是指将图片数据集在空间上组合以生成空间连续的图片的过程。在 Earth Engine 中，这两个术语可以互换使用，但都支持合成和拼接。例如，假设您要合成同一地点的多张图片。例如，使用同一 National Agriculture Imagery Program (NAIP) 数字正射图四分之一四边形 (DOQQ) 在不同时间获取的图像，下例演示了如何制作最大值合成图：

Code Editor (JavaScript)

// Load three NAIP quarter quads in the same location, different times.
var naip2004_2012 = ee.ImageCollection('USDA/NAIP/DOQQ')
  .filterBounds(ee.Geometry.Point(-71.08841, 42.39823))
  .filterDate('2004-07-01', '2012-12-31')
  .select(['R', 'G', 'B']);

// Temporally composite the images with a maximum value function.
var composite = naip2004_2012.max();
Map.setCenter(-71.12532, 42.3712, 12);
Map.addLayer(composite, {}, 'max value composite');

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)

# Load three NAIP quarter quads in the same location, different times.
naip_2004_2012 = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(ee.Geometry.Point(-71.08841, 42.39823))
    .filterDate('2004-07-01', '2012-12-31')
    .select(['R', 'G', 'B'])
)

# Temporally composite the images with a maximum value function.
composite = naip_2004_2012.max()
m.set_center(-71.12532, 42.3712, 12)
m.add_layer(composite, {}, 'max value composite')
m

假设您需要同时拼接四个不同位置的 DOQQ。以下示例演示了使用 imageCollection.mosaic() 时：

Code Editor (JavaScript)

// Load four 2012 NAIP quarter quads, different locations.
var naip2012 = ee.ImageCollection('USDA/NAIP/DOQQ')
  .filterBounds(ee.Geometry.Rectangle(-71.17965, 42.35125, -71.08824, 42.40584))
  .filterDate('2012-01-01', '2012-12-31');

// Spatially mosaic the images in the collection and display.
var mosaic = naip2012.mosaic();
Map.setCenter(-71.12532, 42.3712, 12);
Map.addLayer(mosaic, {}, 'spatial mosaic');

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)

# Load four 2012 NAIP quarter quads, different locations.
naip_2012 = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(
        ee.Geometry.Rectangle(-71.17965, 42.35125, -71.08824, 42.40584)
    )
    .filterDate('2012-01-01', '2012-12-31')
)

# Spatially mosaic the images in the collection and display.
mosaic = naip_2012.mosaic()
m = geemap.Map()
m.set_center(-71.12532, 42.3712, 12)
m.add_layer(mosaic, {}, 'spatial mosaic')

请注意，前面的示例中的 DOQQ 存在一些重叠。mosaic() 方法会根据图片在集合中的顺序（最后一个图片在顶部）合成重叠的图片。如需控制拼接图（或合成图）中的像素来源，请使用图片蒙版。例如，以下代码使用光谱指数阈值来掩盖拼接图像中的数据：

Code Editor (JavaScript)

// Load a NAIP quarter quad, display.
var naip = ee.Image('USDA/NAIP/DOQQ/m_4207148_nw_19_1_20120710');
Map.setCenter(-71.0915, 42.3443, 14);
Map.addLayer(naip, {}, 'NAIP DOQQ');

// Create the NDVI and NDWI spectral indices.
var ndvi = naip.normalizedDifference(['N', 'R']);
var ndwi = naip.normalizedDifference(['G', 'N']);

// Create some binary images from thresholds on the indices.
// This threshold is designed to detect bare land.
var bare1 = ndvi.lt(0.2).and(ndwi.lt(0.3));
// This detects bare land with lower sensitivity. It also detects shadows.
var bare2 = ndvi.lt(0.2).and(ndwi.lt(0.8));

// Define visualization parameters for the spectral indices.
var ndviViz = {min: -1, max: 1, palette: ['FF0000', '00FF00']};
var ndwiViz = {min: 0.5, max: 1, palette: ['00FFFF', '0000FF']};

// Mask and mosaic visualization images.  The last layer is on top.
var mosaic = ee.ImageCollection([
  // NDWI > 0.5 is water.  Visualize it with a blue palette.
  ndwi.updateMask(ndwi.gte(0.5)).visualize(ndwiViz),
  // NDVI > 0.2 is vegetation.  Visualize it with a green palette.
  ndvi.updateMask(ndvi.gte(0.2)).visualize(ndviViz),
  // Visualize bare areas with shadow (bare2 but not bare1) as gray.
  bare2.updateMask(bare2.and(bare1.not())).visualize({palette: ['AAAAAA']}),
  // Visualize the other bare areas as white.
  bare1.updateMask(bare1).visualize({palette: ['FFFFFF']}),
]).mosaic();
Map.addLayer(mosaic, {}, 'Visualization mosaic');

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)

# Load a NAIP quarter quad, display.
naip = ee.Image('USDA/NAIP/DOQQ/m_4207148_nw_19_1_20120710')
m = geemap.Map()
m.set_center(-71.0915, 42.3443, 14)
m.add_layer(naip, {}, 'NAIP DOQQ')

# Create the NDVI and NDWI spectral indices.
ndvi = naip.normalizedDifference(['N', 'R'])
ndwi = naip.normalizedDifference(['G', 'N'])

# Create some binary images from thresholds on the indices.
# This threshold is designed to detect bare land.
bare_1 = ndvi.lt(0.2).And(ndwi.lt(0.3))
# This detects bare land with lower sensitivity. It also detects shadows.
bare_2 = ndvi.lt(0.2).And(ndwi.lt(0.8))

# Mask and mosaic visualization images. The last layer is on top.
mosaic = ee.ImageCollection([
    # NDWI > 0.5 is water. Visualize it with a blue palette.
    ndwi.updateMask(ndwi.gte(0.5)).visualize(
        min=0.5, max=1, palette=['00FFFF', '0000FF']
    ),
    # NDVI > 0.2 is vegetation. Visualize it with a green palette.
    ndvi.updateMask(ndvi.gte(0.2)).visualize(
        min=-1, max=1, palette=['FF0000', '00FF00']
    ),
    # Visualize bare areas with shadow (bare_2 but not bare_1) as gray.
    bare_2.updateMask(bare_2.And(bare_1.Not())).visualize(palette=['AAAAAA']),
    # Visualize the other bare areas as white.
    bare_1.updateMask(bare_1).visualize(palette=['FFFFFF']),
]).mosaic()
m.add_layer(mosaic, {}, 'Visualization mosaic')
m

如需创建一个可最大限度放大输入中的任意频段的复合图像，请使用 imageCollection.qualityMosaic()。qualityMosaic() 方法会根据集合中哪张图片在指定波段中的值最大来设置合成图片中的每个像素。例如，以下代码演示了如何制作最绿的像素复合图和近期值复合图：

Code Editor (JavaScript)

// Define a function that scales and masks Landsat 8 surface reflectance images.
function prepSrL8(image) {
  // Develop masks for unwanted pixels (fill, cloud, cloud shadow).
  var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0);
  var saturationMask = image.select('QA_RADSAT').eq(0);

  // Apply the scaling factors to the appropriate bands.
  var getFactorImg = function(factorNames) {
    var factorList = image.toDictionary().select(factorNames).values();
    return ee.Image.constant(factorList);
  };
  var scaleImg = getFactorImg([
    'REFLECTANCE_MULT_BAND_.|TEMPERATURE_MULT_BAND_ST_B10']);
  var offsetImg = getFactorImg([
    'REFLECTANCE_ADD_BAND_.|TEMPERATURE_ADD_BAND_ST_B10']);
  var scaled = image.select('SR_B.|ST_B10').multiply(scaleImg).add(offsetImg);

  // Replace original bands with scaled bands and apply masks.
  return image.addBands(scaled, null, true)
    .updateMask(qaMask).updateMask(saturationMask);
}

// This function masks clouds and adds quality bands to Landsat 8 images.
var addQualityBands = function(image) {
  // Normalized difference vegetation index.
  var ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']);
  // Image timestamp as milliseconds since Unix epoch.
  var millis = ee.Image(image.getNumber('system:time_start'))
                   .rename('millis').toFloat();
  return prepSrL8(image).addBands([ndvi, millis]);
};

// Load a 2014 Landsat 8 ImageCollection.
// Map the cloud masking and quality band function over the collection.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2014-06-01', '2014-12-31')
  .map(addQualityBands);

// Create a cloud-free, most recent value composite.
var recentValueComposite = collection.qualityMosaic('millis');

// Create a greenest pixel composite.
var greenestPixelComposite = collection.qualityMosaic('nd');

// Display the results.
Map.setCenter(-122.374, 37.8239, 12); // San Francisco Bay
var vizParams = {bands: ['SR_B5', 'SR_B4', 'SR_B3'], min: 0, max: 0.4};
Map.addLayer(recentValueComposite, vizParams, 'Recent value composite');
Map.addLayer(greenestPixelComposite, vizParams, 'Greenest pixel composite');

// Compare to a cloudy image in the collection.
var cloudy = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140825');
Map.addLayer(cloudy, {bands: ['B5', 'B4', 'B3'], min: 0, max: 0.4}, 'Cloudy');

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)

# Define a function that scales and masks Landsat 8 surface reflectance images.
def prep_sr_l8(image):
  # Develop masks for unwanted pixels (fill, cloud, cloud shadow).
  qa_mask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0)
  saturation_mask = image.select('QA_RADSAT').eq(0)

  # Helper function to create image from scaling factors.
  def get_factor_img(factor_names):
    factor_list = image.toDictionary().select(factor_names).values()
    return ee.Image.constant(factor_list)

  # Apply the scaling factors to the appropriate bands.
  scale_img = get_factor_img(
      ['REFLECTANCE_MULT_BAND_.|TEMPERATURE_MULT_BAND_ST_B10']
  )
  offset_img = get_factor_img(
      ['REFLECTANCE_ADD_BAND_.|TEMPERATURE_ADD_BAND_ST_B10']
  )
  scaled = image.select('SR_B.|ST_B10').multiply(scale_img).add(offset_img)

  # Replace original bands with scaled bands and apply masks.
  return (
      image.addBands(scaled, None, True)
      .updateMask(qa_mask)
      .updateMask(saturation_mask)
  )


# This function masks clouds and adds quality bands to Landsat 8 images.
def add_quality_bands(image):
  # Normalized difference vegetation index.
  ndvi = image.normalizedDifference(['SR_B5', 'SR_B4'])
  # Image timestamp as milliseconds since Unix epoch.
  millis = (
      ee.Image(image.getNumber('system:time_start')).rename('millis').toFloat()
  )
  return prep_sr_l8(image).addBands([ndvi, millis])


# Load a 2014 Landsat 8 ImageCollection.
# Map the cloud masking and quality band function over the collection.
collection = (
    ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
    .filterDate('2014-06-01', '2014-12-31')
    .map(add_quality_bands)
)

# Create a cloud-free, most recent value composite.
recent_value_composite = collection.qualityMosaic('millis')

# Create a greenest pixel composite.
greenest_pixel_composite = collection.qualityMosaic('nd')

# Display the results.
m = geemap.Map()
m.set_center(-122.374, 37.8239, 12)  # San Francisco Bay
viz_params = {'bands': ['SR_B5', 'SR_B4', 'SR_B3'], 'min': 0, 'max': 0.4}
m.add_layer(recent_value_composite, viz_params, 'Recent value composite')
m.add_layer(greenest_pixel_composite, viz_params, 'Greenest pixel composite')

# Compare to a cloudy image in the collection.
cloudy = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140825')
m.add_layer(
    cloudy, {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.4}, 'Cloudy'
)
m

使用检查器工具可检查合成图片中不同位置的像素值。 请注意，millis 波段（时间戳）因位置而异，这表明不同的像素来自不同的时间。