AI-generated Key Takeaways
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ImageCollection.reduceToImagecreates an image from a feature collection by applying a reducer over selected properties of features intersecting each pixel. -
This method is used to convert feature collection properties into pixel values based on spatial intersection and a specified reduction.
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The method requires specifying the properties to select from each feature and a reducer to combine these properties into a final pixel value.
| Usage | Returns |
|---|---|
ImageCollection.
reduceToImage
(properties, reducer)
|
Image |
| Argument | Type | Details |
|---|---|---|
|
this:
collection
|
FeatureCollection | Feature collection to intersect with each output pixel. |
properties
|
List | Properties to select from each feature and pass into the reducer. |
reducer
|
Reducer | A Reducer to combine the properties of each intersecting feature into a final result to store in the pixel. |
Examples
Code Editor (JavaScript)
var col = ee . ImageCollection ( 'LANDSAT/LC08/C02/T1_TOA' ) . filterBounds ( ee . Geometry . BBox ( - 124.0 , 43.2 , - 116.5 , 46.3 )) . filterDate ( '2021' , '2022' ); // Image visualization settings. var visParams = { bands : [ 'B4' , 'B3' , 'B2' ], min : 0.01 , max : 0.25 }; Map . addLayer ( col . mean (), visParams , 'RGB mean' ); // Reduce the geometry (footprint) of images in the collection to an image. // Image property values are applied to the pixels intersecting each // image's geometry and then a per-pixel reduction is performed according // to the selected reducer. Here, the image cloud cover property is assigned // to the pixels intersecting image geometry and then reduced to a single // image representing the per-pixel mean image cloud cover. var meanCloudCover = col . reduceToImage ({ properties : [ 'CLOUD_COVER' ], reducer : ee . Reducer . mean () }); Map . setCenter ( - 119.87 , 44.76 , 6 ); Map . addLayer ( meanCloudCover , { min : 0 , max : 50 }, 'Cloud cover mean' );
import ee import geemap.core as geemap
Colab (Python)
col = ( ee . ImageCollection ( 'LANDSAT/LC08/C02/T1_TOA' ) . filterBounds ( ee . Geometry . BBox ( - 124.0 , 43.2 , - 116.5 , 46.3 )) . filterDate ( '2021' , '2022' ) ) # Image visualization settings. vis_params = { 'bands' : [ 'B4' , 'B3' , 'B2' ], 'min' : 0.01 , 'max' : 0.25 } m = geemap . Map () m . add_layer ( col . mean (), vis_params , 'RGB mean' ) # Reduce the geometry (footprint) of images in the collection to an image. # Image property values are applied to the pixels intersecting each # image's geometry and then a per-pixel reduction is performed according # to the selected reducer. Here, the image cloud cover property is assigned # to the pixels intersecting image geometry and then reduced to a single # image representing the per-pixel mean image cloud cover. mean_cloud_cover = col . reduceToImage ( properties = [ 'CLOUD_COVER' ], reducer = ee . Reducer . mean () ) m . set_center ( - 119.87 , 44.76 , 6 ) m . add_layer ( mean_cloud_cover , { 'min' : 0 , 'max' : 50 }, 'Cloud cover mean' ) m
