Google Earth Engine Tutorial: Download Landsat 8 for ArcGIS

Google Earth Engine (GEE) is a powerful platform for geospatial data analysis, offering access to a vast archive of satellite imagery, including Landsat 8. While GEE provides tools for cloud-based processing, many users prefer to download data for direct use in desktop applications like ArcGIS. Below is a step-by-step tutorial to help you extract and download Landsat 8 data from GEE for integration into ArcGIS.

Step 1: Set Up Your Environment

Ensure you have a Google Earth Engine account and access to the Code Editor. Install the necessary libraries, such as the geemap Python package, which simplifies data extraction and visualization. If not already installed, use the following command:

pip install geemap

Step 2: Define Your Area of Interest (AOI)

Use the GEE Code Editor to draw your AOI on the map or input a specific geometry. Replace the placeholder with your coordinates or load a shapefile.

var aoi = ee.Geometry.Rectangle([yourminX, yourminY, yourmaxX, yourmaxY]);

Step 3: Load Landsat 8 Data

Filter the Landsat 8 Collection using date ranges and cloud cover. For example:

var landsat8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2020-01-01', '2020-12-31')
  .filter(ee.Filter.lt('CLOUD_COVER', 5));

Step 4: Select and Export the Image

Choose the desired bands (e.g., RGB for visualization) and export the data as a GeoTIFF. Adjust parameters like region, scale, and projection:

var image = ee.Image(landsat8.first());
var exportImage = image.select(['SR_B.2', 'SR_B.3', 'SR_B.4']); // Example red, green, blue bands
Export.image.toDrive({
  image: exportImage,
  description: 'Landsat8_ArcGIS',
  folder: 'GEE_Exports',
  fileNamePrefix: 'landsat8_arcgis',
  region: aoi,
  scale: 30,
  crs: 'EPSG:4326',
  maxPixels: 1e10
});

Step 5: Download and Import into ArcGIS

After the export completes, download the GeoTIFF file from Google Drive. Open ArcGIS, add the file as a raster layer, and verify the projection and coordinate system match your project requirements.

Supported Bands and Formats

• SR_B1 (Blue): 482.3-532.3 nm
• SR_B2 (Green): 533-590 nm
• SR_B3 (Red): 636-673 nm
• SR_B4 (NIR): 851-879 nm
• SR_B5 (SWIR1): 1560-1660 nm
• SR_B6 (SWIR2): 2100-2290 nm

Additional Tips

If you need to combine multiple images, use ee.ImageCollection.reduce() for mosaics or composites. For large datasets, consider using Export.image.toDrive() with a loop to iterate through the collection.