Guided usage exercise

Step 1: setup your environment

https://openeo.org/documentation/1.0/python/#installation

This exercise will use a Jupyter notebook, but it's also possible to copy paste the code into a regular script.

Managed Jupyter notebook environments can also be used, this may require you to register for an account. Example: https://notebooks.terrascope.be Note that all you need is Python > 3.6 and the ability to install packages.

Step 2: Get the notebook

https://github.com/Open-EO/openeo-usecases/blob/master/vito_phenology_usecase/notebook/data_fusion.ipynb

This notebook runs on the backend at https://openeo.vito.be/openeo/ Other backends may work, but keep in mind that names of data collections and supported processes will need to be adjusted!

Find your preferred backend at: https://hub.openeo.org/

Step 3: Connect and authenticate

Information about demo credentials will be provided in the session. Future warning: backends will allow you to log in with your institution (edugain) or social accounts. This will make demo credentials invalid. Always avoid sharing your credentials with others!

Try to run the initial connection steps in the notebook or your IDE.

Step 4 Try to download a very simple example

The notebook constructs a complex workflow, but when developing and debugging, you often want to check intermediate results. Please try this.

Tip: this notebook works on Terrascope data. You can explore the Sentinel-2 layer here: https://viewer.terrascope.be/?language=en&bbox=2.798217823728918,50.43992723836223,6.313842823728918,51.59490312270836&overlay=true&bgLayer=Satellite&date=2018-05-06&layer=CGS_S2_RADIOMETRY

4.1 Download image

Download a small area of the NDVI datacube (S2ndvi). Tip: 2018-05-06 has nice cloud free data!

You will have to choose a format:

• GTiff (GeoTiff) is suitable for single dates, but does not support timeseries very well. (It will return data for some date.)
• NetCDF does support full timeseries, but the format is somewhat more complex.

4.2 Download timeseries

Downloading an aggregated timeseries for an area of interest is another great way to explore a dataset:

https://open-eo.github.io/openeo-python-client/basics.html#example-retrieving-aggregated-timeseries

It can give you a sense of how much data is available, and how the signal behaves for your area of interest.