加权缩减

默认情况下，应用于图像的 reducer 会根据遮罩值对输入进行加权。 这与通过 clip() 等操作创建的部分像素相关。您可以通过对 reducer 调用 unweighted() 来调整此行为。使用无权重的 reducer 会强制该区域中的所有像素具有相同的权重。以下示例展示了像素权重如何影响 reducer 输出：

Code Editor (JavaScript)

// Load a Landsat 8 input image.
var image = ee.Image('LANDSAT/LC08/C02/T1/LC08_044034_20140318');

// Create an arbitrary region.
var geometry = ee.Geometry.Rectangle(-122.496, 37.532, -121.554, 37.538);

// Make an NDWI image.  It will have one band named 'nd'.
var ndwi = image.normalizedDifference(['B3', 'B5']);

// Compute the weighted mean of the NDWI image clipped to the region.
var weighted = ndwi.clip(geometry)
  .reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: geometry,
    scale: 30})
  .get('nd');

// Compute the UN-weighted mean of the NDWI image clipped to the region.
var unweighted = ndwi.clip(geometry)
  .reduceRegion({
    reducer: ee.Reducer.mean().unweighted(),
    geometry: geometry,
    scale: 30})
  .get('nd');

// Observe the difference between weighted and unweighted reductions.
print('weighted:', weighted);
print('unweighted', unweighted);

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 Landsat 8 input image.
image = ee.Image('LANDSAT/LC08/C02/T1/LC08_044034_20140318')

# Create an arbitrary region.
geometry = ee.Geometry.Rectangle(-122.496, 37.532, -121.554, 37.538)

# Make an NDWI image.  It will have one band named 'nd'.
ndwi = image.normalizedDifference(['B3', 'B5'])

# Compute the weighted mean of the NDWI image clipped to the region.
weighted = (
    ndwi.clip(geometry)
    .reduceRegion(reducer=ee.Reducer.mean(), geometry=geometry, scale=30)
    .get('nd')
)

# Compute the UN-weighted mean of the NDWI image clipped to the region.
unweighted = (
    ndwi.clip(geometry)
    .reduceRegion(
        reducer=ee.Reducer.mean().unweighted(), geometry=geometry, scale=30
    )
    .get('nd')
)

# Observe the difference between weighted and unweighted reductions.
display('weighted:', weighted)
display('unweighted', unweighted)

结果之所以不同，是因为区域边缘的像素因对 reducer 调用 unweighted() 而收到权重为 1。

为了获得明确加权的输出，最好通过对 reducer 调用 splitWeights() 来明确设置权重。由 splitWeights() 修改的 reducer 接受两个输入，其中第二个输入是权重。以下示例通过计算某个区域的加权平均值常态化差值植生指数 (NDVI) 来说明 splitWeights()，其中权重由云量得分给出（云量越大，权重越低）：

Code Editor (JavaScript)

// Load an input Landsat 8 image.
var image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_186059_20130419');

// Compute cloud score and reverse it such that the highest
// weight (100) is for the least cloudy pixels.
var cloudWeight = ee.Image(100).subtract(
  ee.Algorithms.Landsat.simpleCloudScore(image).select(['cloud']));

// Compute NDVI and add the cloud weight band.
var ndvi = image.normalizedDifference(['B5', 'B4']).addBands(cloudWeight);

// Define an arbitrary region in a cloudy area.
var region = ee.Geometry.Rectangle(9.9069, 0.5981, 10.5, 0.9757);

// Use a mean reducer.
var reducer = ee.Reducer.mean();

// Compute the unweighted mean.
var unweighted = ndvi.select(['nd']).reduceRegion(reducer, region, 30);

// compute mean weighted by cloudiness.
var weighted = ndvi.reduceRegion(reducer.splitWeights(), region, 30);

// Observe the difference as a result of weighting by cloudiness.
print('unweighted:', unweighted);
print('weighted:', weighted);

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 an input Landsat 8 image.
image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_186059_20130419')

# Compute cloud score and reverse it such that the highest
# weight (100) is for the least cloudy pixels.
cloud_weight = ee.Image(100).subtract(
    ee.Algorithms.Landsat.simpleCloudScore(image).select(['cloud'])
)

# Compute NDVI and add the cloud weight band.
ndvi = image.normalizedDifference(['B5', 'B4']).addBands(cloud_weight)

# Define an arbitrary region in a cloudy area.
region = ee.Geometry.Rectangle(9.9069, 0.5981, 10.5, 0.9757)

# Use a mean reducer.
reducer = ee.Reducer.mean()

# Compute the unweighted mean.
unweighted = ndvi.select(['nd']).reduceRegion(reducer, region, 30)

# compute mean weighted by cloudiness.
weighted = ndvi.reduceRegion(reducer.splitWeights(), region, 30)

# Observe the difference as a result of weighting by cloudiness.
display('unweighted:', unweighted)
display('weighted:', weighted)

请注意，您需要先将 cloudWeight 添加为频段，然后再调用 reduceRegion()。结果表明，由于降低了多云像素的权重，估算的平均 NDVI 较高。