ee.ImageCollection.qualityMosaic

    • ImageCollection.qualityMosaic composites images in a collection using a quality band to determine per-pixel order.

    • The function selects the image with the highest quality band score for each pixel in the resulting mosaic.

    • All bands from the selected image for a given pixel are included in the output.

    • A common use case involves using an inverted cloud probability band as the quality metric to create a best-pixel mosaic.

     Composites all the images in a collection, using a quality band as a per-pixel ordering function.
    Usage Returns
    ImageCollection. qualityMosaic (qualityBand) Image
    Argument Type Details
    this: collection
    		ImageCollection The collection to mosaic.
    qualityBand
    		String The name of the quality band in the collection.

    Examples

    Code Editor (JavaScript) 

     // The goal is to generate a best-pixel mosaic from a collection of 
 // Sentinel-2 images where pixel quality is based on a cloud probability score. 
 // The qualityMosaic() function selects the image (per-pixel) with the HIGHEST 
 // quality-band-score to contribute to the resulting mosaic. All bands from the 
 // selected image (per-pixel) associated with the HIGHEST quality-band-score 
 // are included in the output. 
 // A Sentinel-2 SR image collection (2 months of images at a specific point). 
 var 
  
 col 
  
 = 
  
 ee 
 . 
 ImageCollection 
 ( 
 'COPERNICUS/S2_SR_HARMONIZED' 
 ) 
  
 . 
 filterBounds 
 ( 
 ee 
 . 
 Geometry 
 . 
 Point 
 ( 
 - 
 103.19 
 , 
  
 40.14 
 )) 
  
 . 
 filterDate 
 ( 
 '2020-07-01' 
 , 
  
 '2020-09-01' 
 ); 
 // Because cloud probability ranges from 0 to 100 percent (low to high), we need 
 // to invert the MSK_CLDPRB band values so that low cloud probability pixels 
 // indicate high quality. Here, an inverting function is mapped over the 
 // image collection, the inverted MSK_CLDPRB band is added as a "quality" band. 
 col 
  
 = 
  
 col 
 . 
 map 
 ( 
 function 
 ( 
 img 
 ) 
  
 { 
  
 var 
  
 cldProb 
  
 = 
  
 img 
 . 
 select 
 ( 
 'MSK_CLDPRB' 
 ); 
  
 var 
  
 cldProbInv 
  
 = 
  
 cldProb 
 . 
 multiply 
 ( 
 - 
 1 
 ). 
 rename 
 ( 
 'quality' 
 ); 
  
 return 
  
 img 
 . 
 addBands 
 ( 
 cldProbInv 
 ); 
 }); 
 // Image visualization settings. 
 var 
  
 visParams 
  
 = 
  
 { 
  
 bands 
 : 
  
 [ 
 'B4' 
 , 
  
 'B3' 
 , 
  
 'B2' 
 ], 
  
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 4500 
 }; 
 Map 
 . 
 setCenter 
 ( 
 - 
 103.19 
 , 
  
 40.14 
 , 
  
 9 
 ); 
 Map 
 . 
 addLayer 
 ( 
 col 
 , 
  
 visParams 
 , 
  
 'Collection (for series inspection)' 
 , 
  
 false 
 ); 
 // Generate a best-pixel mosaic from the image collection. 
 var 
  
 img 
  
 = 
  
 col 
 . 
 qualityMosaic 
 ( 
 'quality' 
 ); 
 Map 
 . 
 addLayer 
 ( 
 img 
 , 
  
 visParams 
 , 
  
 'Best-pixel mosaic (by cloud score)' 
 ); 
 // To build the worst-pixel mosaic, according to cloud probability, use the 
 // MSK_CLDPRB band as the quality band (the worst pixels have HIGHEST cloud 
 // probability score). 
 var 
  
 img 
  
 = 
  
 col 
 . 
 qualityMosaic 
 ( 
 'MSK_CLDPRB' 
 ); 
 Map 
 . 
 addLayer 
 ( 
 img 
 , 
  
 visParams 
 , 
  
 'Worst-pixel mosaic (by cloud score)' 
 , 
  
 false 
 );

    Python setup

    See the Python Environment page for information on the Python API and using geemap for interactive development.

     import 
  
 ee 
 import 
  
 geemap.core 
  
 as 
  
 geemap

    Colab (Python) 

     # The goal is to generate a best-pixel mosaic from a collection of 
 # Sentinel-2 images where pixel quality is based on a cloud probability score. 
 # The qualityMosaic() function selects the image (per-pixel) with the HIGHEST 
 # quality-band-score to contribute to the resulting mosaic. All bands from the 
 # selected image (per-pixel) associated with the HIGHEST quality-band-score 
 # are included in the output. 
 # A Sentinel-2 SR image collection (2 months of images at a specific point). 
 col 
 = 
 ( 
 ee 
 . 
 ImageCollection 
 ( 
 'COPERNICUS/S2_SR_HARMONIZED' 
 ) 
 . 
 filterBounds 
 ( 
 ee 
 . 
 Geometry 
 . 
 Point 
 ( 
 - 
 103.19 
 , 
 40.14 
 )) 
 . 
 filterDate 
 ( 
 '2020-07-01' 
 , 
 '2020-09-01' 
 ) 
 ) 
 # Because cloud probability ranges from 0 to 100 percent (low to high), we need 
 # to invert the MSK_CLDPRB band values so that low cloud probability pixels 
 # indicate high quality. Here, an inverting function is mapped over the 
 # image collection, the inverted MSK_CLDPRB band is added as a "quality" band. 
 def 
  
 invertCloudProbabilityBand 
 ( 
 img 
 ): 
 cldProb 
 = 
 img 
 . 
 select 
 ( 
 'MSK_CLDPRB' 
 ) 
 cldProbInv 
 = 
 cldProb 
 . 
 multiply 
 ( 
 - 
 1 
 ) 
 . 
 rename 
 ( 
 'quality' 
 ) 
 return 
 img 
 . 
 addBands 
 ( 
 cldProbInv 
 ) 
 col 
 = 
 col 
 . 
 map 
 ( 
 invertCloudProbabilityBand 
 ) 
 # Image visualization settings. 
 vis_params 
 = 
 { 
 'bands' 
 : 
 [ 
 'B4' 
 , 
 'B3' 
 , 
 'B2' 
 ], 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 4500 
 } 
 m 
 = 
 geemap 
 . 
 Map 
 () 
 m 
 . 
 set_center 
 ( 
 - 
 103.19 
 , 
 40.14 
 , 
 9 
 ) 
 m 
 . 
 add_layer 
 ( 
 col 
 , 
 vis_params 
 , 
 'Collection (for series inspection)' 
 , 
 False 
 ) 
 # Generate a best-pixel mosaic from the image collection. 
 img 
 = 
 col 
 . 
 qualityMosaic 
 ( 
 'quality' 
 ) 
 m 
 . 
 add_layer 
 ( 
 img 
 , 
 vis_params 
 , 
 'Best-pixel mosaic (by cloud score)' 
 ) 
 # To build the worst-pixel mosaic, according to cloud probability, use the 
 # MSK_CLDPRB band as the quality band (the worst pixels have HIGHEST cloud 
 # probability score). 
 img 
 = 
 col 
 . 
 qualityMosaic 
 ( 
 'MSK_CLDPRB' 
 ) 
 m 
 . 
 add_layer 
 ( 
 img 
 , 
 vis_params 
 , 
 'Worst-pixel mosaic (by cloud score)' 
 , 
 False 
 ) 
 m
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