Satellite Imagery Resources

Satellite imagery is a crucial data source for many GIS applications. The University of Chicago’s Research Computing Center (RCC) provides access to various imagery sources for academic researchers.

Available Imagery Sources

Global Coverage

• Landsat Archive
  Resolution: 30m (15m pan-sharpened)
  Temporal Coverage: 1972-Present
  Access: Free through USGS EarthExplorer
  Special Features: Longest continuous global observation record

• Sentinel-2
  Resolution: 10m-60m
  Temporal Coverage: 2015-Present
  Access: Free through Copernicus Open Access Hub
  Special Features: High revisit rate (5 days), 13 spectral bands

• MODIS
  Resolution: 250m-1000m
  Temporal Coverage: 2000-Present
  Access: Free through NASA Earthdata
  Special Features: Daily global coverage, excellent for time series analysis

High-Resolution Imagery

• NAIP (National Agriculture Imagery Program)
  Resolution: 1m
  Temporal Coverage: Annually for agricultural areas
  Access: Free through USDA Geospatial Data Gateway
  Special Features: Leaf-on imagery, 4-band (RGB + NIR)

• PlanetScope
  Resolution: 3m
  Temporal Coverage: 2016-Present
  Access: Subscription-based, available through institutional license
  Special Features: Near-daily global coverage

Specialized Datasets

• ASTER Global DEM
  Resolution: 30m
  Coverage: Global
  Access: Free through NASA Earthdata
  Special Features: Elevation data, suitable for terrain analysis

• Nighttime Lights
  Sources: VIIRS, DMSP-OLS
  Resolution: 500m-2.7km
  Access: Free through NOAA
  Special Features: Human settlement patterns, economic activity indicators

Access Methods

Web Portals

• UChicago Research Computing Portal
  Pre-processed datasets, institutional access to commercial imagery

• Earthdata Search
  NASA’s interface for satellite data discovery and access

• Copernicus Open Access Hub
  European Space Agency’s Sentinel satellite data

• USGS EarthExplorer
  Access to Landsat, historical aerial photography, and more

Programmatic Access

• Google Earth Engine
  Cloud-based platform for planetary-scale analysis

  // Example: Load and display Landsat 8 imagery
  var landsat = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
      .filterDate('2020-01-01', '2021-01-01')
      .filterBounds(geometry);
  

• Microsoft Planetary Computer
  Cloud-optimized geospatial datasets

Processing Tools

• Desktop Software

  • QGIS with SCP Plugin
  • ArcGIS Pro
  • ENVI

• Cloud Platforms

  • Google Earth Engine
  • Microsoft Planetary Computer
  • AWS Earth

• Programming Libraries

  • Python: Rasterio, GDAL, xarray
  • R: terra, stars
  • JavaScript: Geemap, Leaflet

Best Practices

1. Data Selection

   • Choose appropriate resolution for your analysis
   • Consider temporal requirements
   • Check cloud cover and image quality

2. Pre-processing

   • Atmospheric correction
   • Cloud masking
   • Mosaicking and reprojection

3. Storage

   • Use cloud-optimized formats (COG, ZARR)
   • Implement proper metadata documentation
   • Consider data retention policies

Training Resources

• Workshops

  • Introduction to Remote Sensing
  • Time Series Analysis
  • Machine Learning with Satellite Imagery

• Online Courses

  • Coursera: Remote Sensing Specialization
  • edX: Earth Observation from Space
  • NASA ARSET Training

Support

• Consultations

  • Data discovery assistance
  • Processing workflow design
  • Technical troubleshooting

• Documentation

  • Processing guides
  • Code examples
  • Best practices

Data Citation

Please ensure proper attribution when using satellite imagery in your research. Example:

Landsat 8 image courtesy of the U.S. Geological Survey

Contact

For assistance with satellite imagery:

• Email: gis-imagery@uchicago.edu
• Office: Research Computing Center, Room 201
• Hours: Monday-Friday, 9:00 AM - 5:00 PM