陣列排序和縮減

陣列排序功能可用於取得自訂品質馬賽克，這類作業需要根據不同頻帶的值減少圖像頻帶的子集。以下範例會依據 NDVI 排序，然後取得集合中 NDVI 值最高的觀察值子集的平均值：

程式碼編輯器 (JavaScript)

// Define a function that scales and masks Landsat 8 surface reflectance images
// and adds an NDVI band.
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 opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
  var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);

  // Calculate NDVI.
  var ndvi = opticalBands.normalizedDifference(['SR_B5', 'SR_B4'])
      .rename('NDVI');

  // Replace original bands with scaled bands, add NDVI band, and apply masks.
  return image.addBands(opticalBands, null, true)
      .addBands(thermalBands, null, true)
      .addBands(ndvi)
      .updateMask(qaMask)
      .updateMask(saturationMask);
}

// Define an arbitrary region of interest as a point.
var roi = ee.Geometry.Point(-122.26032, 37.87187);

// Load a Landsat 8 surface reflectance collection.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  // Filter to get only imagery at a point of interest.
  .filterBounds(roi)
  // Filter to get only six months of data.
  .filterDate('2021-01-01', '2021-07-01')
  // Prepare images by mapping the prepSrL8 function over the collection.
  .map(prepSrL8)
  // Select the bands of interest to avoid taking up unneeded memory.
  .select('SR_B.|NDVI');

// Convert the collection to an array.
var array = collection.toArray();

// Label of the axes.
var imageAxis = 0;
var bandAxis = 1;

// Get the NDVI slice and the bands of interest.
var bandNames = collection.first().bandNames();
var bands = array.arraySlice(bandAxis, 0, bandNames.length());
var ndvi = array.arraySlice(bandAxis, -1);

// Sort by descending NDVI.
var sorted = bands.arraySort(ndvi.multiply(-1));

// Get the highest 20% NDVI observations per pixel.
var numImages = sorted.arrayLength(imageAxis).multiply(0.2).int();
var highestNdvi = sorted.arraySlice(imageAxis, 0, numImages);

// Get the mean of the highest 20% NDVI observations by reducing
// along the image axis.
var mean = highestNdvi.arrayReduce({
  reducer: ee.Reducer.mean(),
  axes: [imageAxis]
});

// Turn the reduced array image into a multi-band image for display.
var meanImage = mean.arrayProject([bandAxis]).arrayFlatten([bandNames]);
Map.centerObject(roi, 12);
Map.addLayer(meanImage, {bands: ['SR_B6', 'SR_B5', 'SR_B4'], min: 0, max: 0.4});

Python 設定

請參閱「 Python 環境」頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。

import ee
import geemap.core as geemap

Colab (Python)

# Define a function that scales and masks Landsat 8 surface reflectance images
# and adds an NDVI band.
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)

  # Apply the scaling factors to the appropriate bands.
  optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2)
  thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0)

  # Calculate NDVI.
  ndvi = optical_bands.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI')

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


# Define an arbitrary region of interest as a point.
roi = ee.Geometry.Point(-122.26032, 37.87187)

# Load a Landsat 8 surface reflectance collection.
collection = (
    ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
    # Filter to get only imagery at a point of interest.
    .filterBounds(roi)
    # Filter to get only six months of data.
    .filterDate('2021-01-01', '2021-07-01')
    # Prepare images by mapping the prep_sr_l8 function over the collection.
    .map(prep_sr_l8)
    # Select the bands of interest to avoid taking up unneeded memory.
    .select('SR_B.|NDVI')
)

# Convert the collection to an array.
array = collection.toArray()

# Label of the axes.
image_axis = 0
band_axis = 1

# Get the NDVI slice and the bands of interest.
band_names = collection.first().bandNames()
bands = array.arraySlice(band_axis, 0, band_names.length())
ndvi = array.arraySlice(band_axis, -1)

# Sort by descending NDVI.
sorted = bands.arraySort(ndvi.multiply(-1))

# Get the highest 20% NDVI observations per pixel.
num_images = sorted.arrayLength(image_axis).multiply(0.2).int()
highest_ndvi = sorted.arraySlice(image_axis, 0, num_images)

# Get the mean of the highest 20% NDVI observations by reducing
# along the image axis.
mean = highest_ndvi.arrayReduce(reducer=ee.Reducer.mean(), axes=[image_axis])

# Turn the reduced array image into a multi-band image for display.
mean_image = mean.arrayProject([band_axis]).arrayFlatten([band_names])
m = geemap.Map()
m.center_object(roi, 12)
m.add_layer(
    mean_image, {'bands': ['SR_B6', 'SR_B5', 'SR_B4'], 'min': 0, 'max': 0.4}
)
m

如同線性建模範例，請沿著頻帶軸使用 arraySlice()，將感興趣的頻帶與排序索引 (NDVI) 分開。然後使用 arraySort() 依排序索引排序感興趣的頻帶。將像素依 NDVI 值遞減排序後，請沿著 imageAxis 使用 arraySlice()，取得最高 NDVI 值像素的 20%。最後，使用平均縮減器沿著 imageAxis 套用 arrayReduce()，以便取得最高 NDVI 像素的平均值。最後一個步驟是將陣列圖片轉換回多頻帶圖片，以便顯示。