如需枚举两个集合元素之间的所有匹配项，请使用 ee.Join.inner()。内连接的输出是 FeatureCollection（即使将一个 ImageCollection 连接到另一个 ImageCollection）。输出中的每个地图项都代表一个匹配项，其中匹配元素存储在地图项的两个属性中。例如，feature.get('primary') 是主集合中的元素，与存储在 feature.get('secondary') 中的次要集合中的元素匹配。（可以将这些属性的其他名称指定为 inner() 的参数，但 ‘primary’ 和 ‘secondary’ 是默认值）。输出中的多个地图项表示一对多关系。如果任一集合中的元素没有匹配项，则该元素不会出现在输出中。
使用 ImageCollection 输入的联接示例可直接应用于 FeatureCollection 输入。您还可以将 FeatureCollection 联接到 ImageCollection，反之亦然。请考虑以下内部联接示例：
Code Editor (JavaScript)
// Create the primary collection. var primaryFeatures = ee.FeatureCollection([ ee.Feature(null, {foo: 0, label: 'a'}), ee.Feature(null, {foo: 1, label: 'b'}), ee.Feature(null, {foo: 1, label: 'c'}), ee.Feature(null, {foo: 2, label: 'd'}), ]); // Create the secondary collection. var secondaryFeatures = ee.FeatureCollection([ ee.Feature(null, {bar: 1, label: 'e'}), ee.Feature(null, {bar: 1, label: 'f'}), ee.Feature(null, {bar: 2, label: 'g'}), ee.Feature(null, {bar: 3, label: 'h'}), ]); // Use an equals filter to specify how the collections match. var toyFilter = ee.Filter.equals({ leftField: 'foo', rightField: 'bar' }); // Define the join. var innerJoin = ee.Join.inner('primary', 'secondary'); // Apply the join. var toyJoin = innerJoin.apply(primaryFeatures, secondaryFeatures, toyFilter); // Print the result. print('Inner join toy example:', toyJoin);
import ee import geemap.core as geemap
Colab (Python)
# Create the primary collection. primary_features = ee.FeatureCollection([ ee.Feature(None, {'foo': 0, 'label': 'a'}), ee.Feature(None, {'foo': 1, 'label': 'b'}), ee.Feature(None, {'foo': 1, 'label': 'c'}), ee.Feature(None, {'foo': 2, 'label': 'd'}), ]) # Create the secondary collection. secondary_features = ee.FeatureCollection([ ee.Feature(None, {'bar': 1, 'label': 'e'}), ee.Feature(None, {'bar': 1, 'label': 'f'}), ee.Feature(None, {'bar': 2, 'label': 'g'}), ee.Feature(None, {'bar': 3, 'label': 'h'}), ]) # Use an equals filter to specify how the collections match. toy_filter = ee.Filter.equals(leftField='foo', rightField='bar') # Define the join. inner_join = ee.Join.inner('primary', 'secondary') # Apply the join. toy_join = inner_join.apply(primary_features, secondary_features, toy_filter) # Print the result. display('Inner join toy example:', toy_join)
在前面的示例中，请注意，表之间的关系是在过滤条件中定义的，这表示字段 ‘foo’ 和 ‘bar’ 是联接字段。然后，指定并应用内连接到集合。检查输出，您会发现每个可能的匹配都表示为一个 Feature。
下面是一个有益的示例，请考虑联接 MODIS ImageCollection 对象。MODIS 质量数据有时存储在与图像数据分开的集合中，因此内连接非常适合用于联接这两个集合，以便应用质量数据。在本例中，图片获取时间相同，因此 equals 过滤器会负责指定这两个集合之间的这种关系：
Code Editor (JavaScript)
// Make a date filter to get images in this date range. var dateFilter = ee.Filter.date('2014-01-01', '2014-02-01'); // Load a MODIS collection with EVI data. var mcd43a4 = ee.ImageCollection('MODIS/MCD43A4_006_EVI') .filter(dateFilter); // Load a MODIS collection with quality data. var mcd43a2 = ee.ImageCollection('MODIS/006/MCD43A2') .filter(dateFilter); // Define an inner join. var innerJoin = ee.Join.inner(); // Specify an equals filter for image timestamps. var filterTimeEq = ee.Filter.equals({ leftField: 'system:time_start', rightField: 'system:time_start' }); // Apply the join. var innerJoinedMODIS = innerJoin.apply(mcd43a4, mcd43a2, filterTimeEq); // Display the join result: a FeatureCollection. print('Inner join output:', innerJoinedMODIS);
import ee import geemap.core as geemap
Colab (Python)
# Make a date filter to get images in this date range. date_filter = ee.Filter.date('2014-01-01', '2014-02-01') # Load a MODIS collection with EVI data. mcd43a4 = ee.ImageCollection('MODIS/MCD43A4_006_EVI').filter(date_filter) # Load a MODIS collection with quality data. mcd43a2 = ee.ImageCollection('MODIS/006/MCD43A2').filter(date_filter) # Define an inner join. inner_join = ee.Join.inner() # Specify an equals filter for image timestamps. filter_time_eq = ee.Filter.equals( leftField='system:time_start', rightField='system:time_start' ) # Apply the join. inner_joined_modis = inner_join.apply(mcd43a4, mcd43a2, filter_time_eq) # Display the join result: a FeatureCollection. display('Inner join output:', inner_joined_modis)
如需使用输出 FeatureCollection 中的联接图片，请对输出使用组合函数 map()。例如，匹配的图片可以堆叠在一起，以便将质量带添加到图片数据中：
Code Editor (JavaScript)
// Map a function to merge the results in the output FeatureCollection. var joinedMODIS = innerJoinedMODIS.map(function(feature) { return ee.Image.cat(feature.get('primary'), feature.get('secondary')); }); // Print the result of merging. print('Inner join, merged bands:', joinedMODIS);
import ee import geemap.core as geemap
Colab (Python)
# Map a function to merge the results in the output FeatureCollection. joined_modis = inner_joined_modis.map( lambda feature: ee.Image.cat( feature.get('primary'), feature.get('secondary') ) ) # Print the result of merging. display("Inner join, merged 'bands':", joined_modis)
虽然此函数会映射到 FeatureCollection，但结果是 ImageCollection。生成的 ImageCollection 中的每个图片都包含主集合（在此示例中仅为 ‘EVI’）中的所有图片的波段，以及次级集合中匹配图片的所有波段（质量波段）。