Open & Free
Earth Observation

Data Access

SE: Analysis & Modelling [Remote Sensing]
AGI-856.143 | ST 2018

Hannah Augustin
University of Salzburg
Department of Geoinformatics - Z_GIS


  • Landsat v. Sentinel-2
  • USGS Earth Explorer
  • Copernicus Open Access Hub
  • QGIS Plugin: Sentinel Downloader
  • EO-Compass
  • Outlook: ARD & Data Cubes?



  • 15m/30m/100m resolution (Landsat 8)
  • average revisit time of 8-16 days
  • terrain corrected -- requires radiometric calibration
  • organised in paths and rows
  • level-2 data available opon request
  • analysis-ready-data available for U.S.
Landsat's paths, rows and the UTM zones (source)


  • 10m/20m/60m resolution
  • average revisit time of 5-10 days at equator
  • calibrated to TOA and terrain corrected
  • organised in UTM granules
  • swaths and granules differ
  • level-2 selectively available
UTM zones (source)
Sentinel granules (EO-Compass)
Sentinel-2 acquisition plan swaths (EO-Compass)
Spectral comparison of Landsat 7 and 8 and Sentinel 2 (source)

USGS Earth Explorer

Earth Explorer interface
Datasets available on Earth Explorer

Alternatives for Landsat

Copernicus Open Access Hub

Copernicus Open Access Hub landing page
Copernicus Open Access Hub interface

Copernicus Open Access Hub

  • Sentinel 1, 2 and 3
  • cannot search by Sentinel-2 granule

Alternatives for Sentinel

QGIS Plugin

Sentinel Downloader



How do you search for EO images?


area or location of interest

time of interest

data type

metadata (e.g. cloud cover)


How can you describe the contents of an archive?


number of images

data characteristics (e.g. resolution, frequency)

average cloud cover



semantic searches based on image contents

tools to better describe an archive

geovisual tools to display archive coverage and quality

better assess suitability of data source for EO analysis


EO-Compass landing page
EO-Compass: average cloud cover from Sentinel-2 metadata
EO-Compass: satellite status

Analysis Ready Data
Data Cubes

What is a data cube?

A datacube is a massive multi-dimensional array, also called “raster data” or “gridded data”; “massive” entails that we talk about sizes significantly beyond the main memory resources of the server hardware. Data values, all of the same data type, sit at grid points as defined by the d-axes of the d-dimensional datacube. Coordinates along these axes allow addressing data values unambiguously.
- The Datacube Manifesto, © 2017 Peter Baumann

What is analysis ready data?

What is the Open Data Cube initiative?

Open Data Cube

    • international open-source initiative
    • evolved from Australian Geoscience Data Cube (AGDC)
    • better leverage EO potential
    • support global priority agendas (e.g. UN-SDGs)

Partner Projects

Open Data Cube Development

    • Australia
    • Columbia
    • Switzerland
    • United States
    • Taiwan
    • Uganda
    • Vietnam
    • United Kingdom
    • Georgia
    • Moldova
As of February 2018, 3 operational, 7 in-development, 29 under review
Source: ODC from July 2017 presentation


SE: Analysis & Modelling [Remote Sensing]
AGI-856.143 | ST 2018

GitHub: @augustinh22