Landsat Image Mosaic of Antarctica (LIMA) - Processed Landsat Scenes (16 bit) Metadata

USGS/LIMA/SR_METADATA
Dataset Availability
1999-06-30T00:00:00Z–2002-09-04T00:00:00Z
Dataset Provider
Earth Engine Snippet
ee.FeatureCollection("USGS/LIMA/SR_METADATA")
Tags
antarctica ice landsat-derived lima mosaic satellite-imagery sr table usgs

Description

The Landsat Image Mosaic of Antarctica (LIMA) is a seamless and virtually cloudless mosaic created from processed Landsat 7 ETM+ scenes.

Processed Landsat Scenes (16 bit) are Level 1Gt NLAPS scenes converted to 16 bit, processed with sun-angle correction, and converted to reflectance values ( Bindschadler 2008 ).

Each Landsat scene is processed with elevation data and sun-angle correction to ensure surface features were accurately represented. The sun's angle in Antarctica gives the appearance of a setting sun. Because of the low sun angle, as Landsat passes over Antarctica, the outer edges of the continent appear brighter than areas closer to the South Pole, so scenes have bright and dark areas. Inconsistent sun angles and shadows where corrected for these scenes. Without this process, mosaicking would produce a patchwork of scenes since each scene would have a brighter and a darker side.

This is a table which contains metadata for the Image Collection USGS/LIMA/SR

Table Schema

Table Schema

Name Type Description
ACQ_DATE
STRING

Acquisition date in YYYY-MM-DD format

PATH
INT

WRS path

POLY_ID
INT

Unique ID assigned to a polygon

ROW
INT

WRS row

SCENE_ID
STRING

Scene ID

SENSOR
STRING

Sensor

SPACE
STRING

Name of the satellite used to gather data

Terms of Use

Terms of Use

These images are in the public domain and can be used freely and without acknowledgement. However, credit to the Landsat Image Mosaic of Antarctica (LIMA) Project is greatly appreciated.

Citations

Citations:
  • Bindschadler, R., Vornberger, P., Fleming, A., Fox, A., Mullins, J., Binnie, D., Paulson, S., Granneman, B., and Gorodetzky, D., 2008, The Landsat Image Mosaic of Antarctica, Remote Sensing of Environment, 112, pp. 4214-4226. PDF

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Code Editor (JavaScript)

 var 
  
 dataset 
  
 = 
  
 ee 
 . 
 FeatureCollection 
 ( 
 'USGS/LIMA/SR_METADATA' 
 ); 
 // Calculate the age of each feature by subtracting 
 // the acquisition date from "today". 
 var 
  
 feature_ages 
  
 = 
  
 dataset 
 . 
 map 
 ( 
  
 function 
 ( 
 feature 
 ) 
  
 { 
  
 var 
  
 today 
  
 = 
  
 ee 
 . 
 Date 
 . 
 fromYMD 
 ( 
 2024 
 , 
  
 1 
 , 
  
 12 
 ); 
  
 var 
  
 acq_date 
  
 = 
  
 ee 
 . 
 Date 
 . 
 parse 
 ( 
  
 'yyyy-MM-dd' 
 , 
  
 feature 
 . 
 get 
 ( 
 'ACQ_DATE' 
 )); 
  
 var 
  
 diff 
  
 = 
  
 today 
 . 
 difference 
 ( 
 acq_date 
 , 
  
 'day' 
 ); 
  
 return 
  
 feature 
 . 
 set 
 ({ 
 'ACQ_AGE' 
 : 
  
 diff 
 }); 
  
 } 
 ); 
 // Reduce by calculating the smallest ACQ_AGE, 
 // which gives the most recent acquisition date for 
 // that area. 
 var 
  
 reduced_ages 
  
 = 
  
 feature_ages 
 . 
 reduceToImage 
 ({ 
  
 properties 
 : 
  
 [ 
 'ACQ_AGE' 
 ], 
  
 reducer 
 : 
  
 ee 
 . 
 Reducer 
 . 
 min 
 () 
 }); 
 var 
  
 reduced_ages_vis 
  
 = 
  
 { 
  
 min 
 : 
  
 6000 
 , 
  
 max 
 : 
  
 9000 
 , 
  
 palette 
 : 
  
 [ 
 '00ff00' 
 , 
  
 'ff0000' 
 ], 
 }; 
 var 
  
 lon 
  
 = 
  
 - 
 43.6 
 ; 
 var 
  
 lat 
  
 = 
  
 - 
 74.2 
 ; 
 var 
  
 gray 
  
 = 
  
 150 
 ; 
 var 
  
 background 
  
 = 
  
 ee 
 . 
 Image 
 . 
 rgb 
 ( 
 gray 
 , 
  
 gray 
 , 
  
 gray 
 ). 
 visualize 
 ({ 
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 255 
 }); 
 Map 
 . 
 setCenter 
 ( 
 lon 
 , 
  
 lat 
 , 
  
 2 
 ); 
 Map 
 . 
 addLayer 
 ( 
  
 reduced_ages 
 , 
  
 reduced_ages_vis 
 , 
  
 'Acquisition Age' 
 ); 
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