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Array Sorting and Reducing

Array sorting is useful for obtaining custom quality mosaics which involve reducing a subset of image bands according to the values in a different band. The following example sorts by NDVI, then gets the mean of a subset of observations in the collection with the highest NDVI values:

Code Editor (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 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)

 # 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

As in the linear modeling example, separate the bands of interest from the sort index (NDVI) using arraySlice() along the band axis. Then sort the bands of interest by sort index using arraySort() . After the pixels have been sorted by descending NDVI, use arraySlice() along the imageAxis to get 20% of the highest NDVI pixels. Lastly, apply arrayReduce() along the imageAxis with a mean reducer to get the mean of the highest NDVI pixels. The final step converts the array image back to a multi-band image for display.

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Code Editor (JavaScript)

Colab (Python)

Array Sorting and Reducing