Accessing Copernicus Data from Nostradamus Notebooks

Data Discovery & Access
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Accessing Copernicus Data from Notebooks
Accessing Copernicus Data from Notebooks1672×941 437 KB

The NOSTRADAMUS EO Data Hub includes notebooks that show practical ways to discover and access Earth Observation data directly from a notebook environment

One of the first examples now available is the EO_Products_Access.ipynb notebook in the NOSTRADAMUS GitLab repository:

Open the notebook repository

This example is meant as a practical starting point for users who want to work with Copernicus data from Jupyter notebooks and progressively adapt the same approach to their own areas of interest and workflows

What this notebook does

The notebook shows a simple but important workflow for accessing Copernicus Data Space Ecosystem data from Python

It demonstrates how to:

  • define a search period
  • define an area of interest
  • build a query against the CDSE OData catalogue
  • request product metadata from the catalogue
  • load the response into a pandas dataframe
  • inspect the returned EO products
  • visualise the result in a notebook environment

The example focuses on Sentinel-2 products and uses the CDSE catalogue endpoint to search for products matching the selected temporal and spatial criteria

Main elements shown in the notebook

The notebook starts with a few core Python imports, including:

  • requests
  • json
  • pandas
  • os

It then defines a query period, for example:

  • search_period_start
  • search_period_end

and an area of interest expressed as a polygon geometry

The notebook builds a request URL against the CDSE OData catalogue endpoint, using filters such as:

  • collection name
  • time range
  • area of interest

It then sends the request and converts the response into a pandas dataframe using:

  • response = requests.get(url)
  • metadata = response.json()
  • products_df = pd.DataFrame.from_dict(metadata["value"])

The result is a simple and reusable pattern to move from EO catalogue search to analysis inside a notebook

Why this matters in the NOSTRADAMUS EO Data Hub

This example shows one of the key ideas behind the Data Hub

Users should be able to move from data discovery to analysis without breaking their workflow across disconnected environments

A typical path is:

  1. discover a dataset or collection
  2. access metadata and products programmatically
  3. inspect results in a notebook
  4. adapt the same code for a specific use case
  5. integrate the notebook into a larger workflow or application

This is particularly useful in the context of NOSTRADAMUS where notebooks can become the basis for:

  • agricultural monitoring examples
  • demonstration-site workflows
  • validation activities
  • training material
  • reusable application components

How to use this example

A simple way to use this notebook is:

  1. open the repository in GitLab
  2. review the notebook structure and comments
  3. launch a notebook environment through the Application Hub
  4. copy or clone the repository into your working environment
  5. update the area of interest and time range
  6. run the notebook and inspect the results
  7. adapt it to your own project or demo-site activity

Before you start

To run examples like this successfully, make sure that:

  • you have access to a notebook environment
  • you understand the basics of Python notebooks
  • you are familiar with the idea of querying EO catalogues
  • you have the necessary credentials if a workflow requires authenticated access

Depending on how the notebook evolves, some parts may work directly against public catalogue services while others may later require authentication or additional dependencies

Relation to the rest of the Data Hub

This notebook is one piece of the wider NOSTRADAMUS EO Data Hub environment

Different services play different roles:

  • GitLab hosts notebooks, code and reusable resources
  • Application Hub provides the environment where notebooks can be launched
  • Drive can host supporting files, intermediate outputs and shared material
  • Data Discovery & Access topics explain how users can interact with EO and project data sources

Together these services help users move from examples to reproducible workflows

Repository link

NOSTRADAMUS GitLab repository:

https://git.eratosthenes.org.cy/notebooks/cdse_data_access

What comes next

This is only a starting point

Over time, the notebooks area can grow to include additional examples for:

  • Sentinel data access
  • agricultural indicators
  • demonstration-site specific workflows
  • data visualisation
  • data cube preparation
  • validation and analysis notebooks

If you use this notebook and adapt it to your own activity, feel free to share feedback or propose additional examples through the Data Hub