Learning goals

  • Get familiar with Sentinel-2 data characteristics
  • Get to know the Copernicus Open Access Hub
  • Create a data query & download a Sentinel-2 image
  • Understand file formats and folder structure
  • Learn how to prepare a Sentinel-2 image for visualization


Sentinel-2 satellites

The European Union’s Copernicus program features several Earth observing satellites, including a pair of satellites carrying high resolution optical sensors: Sentinel-2A (S2A) and Sentinel-2B (S2B). The satellites were launched recently, S2A in June 2015, S2B in March 2017. The sensor on board is called Multispectral Imager (MSI). Each satellite overpasses the same place on Earth every ten days. Combined, both satellites yield a revisit time of 5 days, recording with 290 km swath width. This is much more as compared to the Landsat sensor family (~185 km, 16-day repeat cycle by one sensor; 8-day repeat cycle by two sensors).

Comparison of Sentinel 2 with Landsat 7 and 8 spectral bands (Source: USGS).

The Sentinel-2 MSI has 13 spectral bands, which partly resemble the Landsat spectral bands but also add new spectral regions, such as the red edge bands. The bands have varying spatial resolutions of 10, 20, or 60 m. The three visible bands blue, green, red, and a broad nIR band come at 10 m spatial resolution (bands 2, 3, 4, and 8). Red edge bands, a narrow nIR band, and the swIR bands come at 20 m spatial resolution (bands 5, 6, 7, 8a, 11, and 12).

Sentinel-2 products

Sentinel-2 products are delivered in various processing levels:

Name Abbreviation High-level description
Level 1B L1B Top-of-atmosphere radiance values in sensor geometry
Level 1C L1C Top-of-atmosphere reflectance in cartographic geometry
Level 2A L2A Bottom-of-atmosphere reflectance in cartographic geometry

Most applications use either L1C or L2A products as they are already processed into usable quality in terms of radiometry and geometry. The L1C and L2A products are partitioned into “granules”, which represent 100x100 km image chips in UTM/WGS84 projection. You can download the grid in .kml format here.

Sentinel-2 granules (Source: ESA)

Data providers

Image archives such as the Sentinel-2, Landsat or MODIS data catalog represent enormous data volumes, growing bigger every day. Who stores the data and how is it made accessible to users?

Public institutions such as the the European Space Agency (ESA) and the United States Geological Survey (USGS) play a key role in this regard, as they host the currently most relevant platforms for Earth Observation data distribution - the Copernicus Open Access Hub and the USGS Earth Explorer. Most (but not all) of the data provided on these platforms are currently available free of charge.

Copernicus Open Access Hub and USGS Earth Explorer.

Session materials

Download the session materials from our shared repository.

If you experience issues with slow internet connection during the Sentinel-2 data download from the Copernicus Open Access Hub, please download the image for this exercise (>1GB) in advance here.


This exercise will guide you through the basic steps of Sentinel-2 data acquisition and some basic steps to prepare the Sentinel-2 image for visualization in the EnMAP Box.

Acquire Sentinel-2 data

The Copernicus Open Access Hub allows you to search for data without registering, but in order to download the data, you will need to create an account. Use the browser interface to search for images as demonstrated in the video below:

  • Please search for images covering the greater area of Berlin with the following specifications:

    • Sensing period: 01/05/2019 - 31/10/2019
    • Mission: Sentinel-2
    • Product Type: Surface Reflectance (Level 2A, S2MSI2A)
    • Cloud cover: max. 20%
  • How many images are available for your query? Why may it vary between team members?

  • Download the image covering granule T33UUU, acquired 26/07/2019. If you cannot download the image, you can use the alternative download link under session materials.

  • Please note that the Windows operating system imposes a maximum filename length (file path + filename) of 255 characters. The downloaded Sentinel-2 image comes along with a complex folder structure and relatively long filenames. You may therefore initially store the download at the top level of your folder structure until you have prepared the data.

Unpack Sentinel-2 data

  • The downloaded Sentinel-2 image is a .zip-file. Unzip it and delete the .zip-file after unzipping has successfully completed.

  • Take a look at the folder structure and the files contained in the folders. The image data is organized by the spatial resolution of the input bands. The video below guides these steps.

Prepare Sentinel-2 data

We now want to stack the single bands of the Sentinel-2 image into a single multiband Sentinel-2 image in the ENVI-format using the Virtual Raster Builder.

  • Open QGIS and install the ‘Virtual Raster Builder’ plugin.

  • Open the EnMAP-Box and load ‘S2_Subset_Berlin.shp’ into Data Sources. The shapefile will be used to clip the Sentinel-2 scene to a smaller extent.

  • Open the ‘Virtual Raster Builder’ from the EnMAP-Box. Follow the video instructions to create a multiband-image with the following specifications:

    • Spectral Bands: B2, B3, B4, B5, B6, B7, B8a, B11, B12
    • Spatial Resolution: 20m (from the ‘R20M’ file)
    • Spatial Extent: according to the shapefile “S2_Subset_Berlin.shp”
    • Format: ENVI

Edit Sentinel-2 metadata

  • Visualize the multiband Sentinel-2 image in the EnMAP-Box with the band combination R = nIR, G = red, B = green.

  • Visualize an image spectrum. Which meta-information is missing?

  • Remove the prepared Sentinel-2 image from the Data Source Panel. Also delete the .aux file and .vrt file from the folder.

  • Add the Sentinel-2 wavelengths and the wavelengths units to the metadata .hdr file.

  • Reload the Sentinel-2 image into the Data Source Panel. The wavelengths should now be displayed on the x-axis when visualizing image spectra. Now you can compare spectra obtained in different wavelength regions, e.g. acquired by different sensors.


The aim of this assignment is compare the spectral and spatial characteristics of Sentinel-2 with corresponding characteristics of HyMap data.

Sentinel-2 vs. HyMap

  • What are the differences between Sentinel-2 and the HyMap image from last week regarding:

    • Acquisition platform
    • Acquisition date
    • Spatial resolution
    • Number of spectral bands

Compare Sentinel-2 & HyMap

  • Visualize the Sentinel-2 and the HyMap images in appropriate RGB band combinations in two separate MapViews and link both images by location and zoom level as shown in the video below.
  • For each of the classes below, find one ‘pure’ surface example (homogeneous patch, no mixtures) which is has not changed between both image acquisition dates:
    • Building
    • Sealed non built-up
    • Coniferous forest
    • Deciduous forest
    • Grass
    • Bare land (soil/sand)
    • Water
  • Compare each surface both spatially and spectrally and describe
    • differences due to the different spatial resolutions
    • differences due to the different spectral characteristics
  • Capture screenshots of the images and spectra for each example and add a short description for both points, following the layout suggestion below.

Assignment layout template


  • Please upload the comparison between the Sentinel-2 and the HyMap image as pdf to moodle.

  • General submission notes: Submission deadline for the weekly assignment is always the following Monday at 10am. Please use the naming convention indicating session number and family name of all students in the respective team, e.g. ‘s01_surname1_surname2_surname3_surname4.pdf’. Each team member has to upload the assignment individually. Provide single file submissions, in case you have to submit multiple files, create a *.zip archive.

Copyright © 2020 Humboldt-Universität zu Berlin. Department of Geography.