Here are some Jupyter notebooks and python scripts to prepare the satellite and aerial data to reconstruct fire spread.
For any questions about this repository, please contact ops@eumetsat.int.
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This code is licensed under GPL-3.0-or-later license. See file LICENSE.txt for details on the usage and distribution terms.
All product names, logos, and brands are property of their respective owners. All company, product and service names used in this website are for identification purposes only.
- Dominika Leskow-Czyzewska - Initial work and SW resposible - EUMETSAT
- Andrea Meraner - Initial work - EUMETSAT
- P. Sánchez - Contributor to JNs for airborne data - Universitat Autonoma de Barcelona
- I. González - Contributor to JNs for airborne data - Universitat Autonoma de Barcelona
Please see the AUTHORS.txt file for more information on contributors.
If you use this software in your research or projects, we kindly ask you to acknowledge our work by citing the following reference:
Sánchez, P., González, I., Cortés, A., Carrillo, C., & Margalef, T. (2025). Airborne Hyperspectral Data Processing Software. Zenodo. https://doi.org/10.5281/zenodo.14871861
This software was developed as part of the SALUS (CPP2021-008762) framework.
You will require Jupyter Notebook to run this code. We recommend that you install
the latest Anaconda Python distribution for your
operating system. Anaconda Python distributions include Jupyter Notebook.
| item | version | licence | copyright | package info |
|---|---|---|---|---|
| earthaccess | 0.13.0 | MIT | 2021 Luis Lopez | https://anaconda.org/conda-forge/earthaccess |
| eocanvas | 0.2.4 | Apache 2.0 | - | https://pypi.org/project/eocanvas/ |
| eumdac | 3.0.0 | MIT | EUMETSAT | https://anaconda.org/eumetsat/eumdac |
| fiona | 1.9.6 | BSD-3-Clause | 2007, Sean C. Gillies | https://anaconda.org/conda-forge/fiona |
| gdal | 3.11.0 | MIT | - | https://anaconda.org/conda-forge/gdal |
| geopandas | 1.0.1 | BSD-3-Clause | 2013-2022, GeoPandas developers. | https://anaconda.org/conda-forge/geopandas |
| hda | 2.20 | Apache-2.0 | - | https://pypi.org/project/hda |
| ipywidgets | 8.1.5 | BSD-3-Clause | 2015 Project Jupyter Contributors | https://anaconda.org/conda-forge/ipywidgets |
| jupyterlab | 4.3.4 | BSD-3-Clause | 2015-2024 Project Jupyter Contributors | https://anaconda.org/conda-forge/jupyterlab |
| matplotlib | 3.9.2 | PSF | 2012- Matplotlib Development Team | https://matplotlib.org/stable/users/project/license.html |
| numpy | 1.26.4 | BSD-3-Clause | 2005-2023, NumPy Developers. | https://anaconda.org/conda-forge/numpy |
| openeo | 0.37.0 | Apache-2.0 | - | https://anaconda.org/conda-forge/openeo |
| pandas | 2.2.2 | BSD-3-Clause | 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team | https://anaconda.org/conda-forge/pandas |
| pystac | 1.12.1 | Apache-2.0 | - | https://anaconda.org/conda-forge/pystac |
| python | 3.11 | PSF | 2001-2023 Python Software Foundation | https://docs.python.org/3/license.html |
| rasterio | 1.4.3 | BSD-3-Clause | 2013-2021, Mapbox | https://anaconda.org/conda-forge/rasterio |
| rioxarray | 0.17.0 | Apache-2.0 | - | https://anaconda.org/conda-forge/rioxarray |
| satpy | 0.54.0 | GPL-3.0-or-later | 2021 Satpy developers | https://anaconda.org/conda-forge/satpy/ |
| shapely | 2.0.4 | BSD-3-Clause | 2007, Sean C. Gillies. 2019, Casper van der Wel. 2007-2022, Shapely Contributors. | https://anaconda.org/conda-forge/shapely |
| urllib3 | 2.2.2 | MIT | 2008-2020 Andrey Petrov and contributors. | https://anaconda.org/conda-forge/urllib3/ |
| xarray | 2024.2.0 | Apache-2.0 | - | https://anaconda.org/conda-forge/xarray |
None
The simplest and best way to install these packages is via Git. Users can clone this repository by running the following commands from either their terminal (on Linux/OSx), or from the Anaconda prompt.
You can usually find your terminal in the start menu of most Linux distributions and in the Applications/Utilities folder on OSx. Alternatively, you should be able to find/open your Anaconda prompt from your start menu (or dock, or via running the Anaconda Navigator). Once you have opened a terminal/prompt, you should navigate to the directory where you want to put the code. Once you are in the correct directory, you should run the following command;
git clone --recurse-submodules --remote-submodules https://gitlab.eumetsat.int/eumetlab/atmosphere/trainings/nero-winter-school-2025.git
This will make a local copy of all the relevant files.
Note: If you find that you are missing packages, you should check that you ran
git clone with both the --recurse-submodules and --remote-submodules options.
Note: if you are using an older version of git, you may find that your submodules are empty.
In this case, you need to remove the folder and re-run the line above with --recursive added to the end
Note: in some rare Anaconda instances, Git is not installed by default. To correct
this, you can install Git using conda install git from the Anaconda prompt (Windows)
or in your terminal (OSx/Linux).
This collection supports Python 3.11. Although many options are possible, the authors highly recommend that users install the appropriate Anaconda package for their operating system. In order to ensure that you have all the required dependencies, we recommend that you build a suitable Python environment, as discussed below.
Python allows users to create specific environments that suit their applications. This tutorials included in this collection require a number of non-standard packages - e.g. those that are not included by default in Anaconda. In this directory, users will find a environment.yaml file which can be used to construct an environment that will install all the required packages.
To construct the environment, you should open either terminal (Linux/OSx) or an Anaconda prompt window and navigate to repository folder you downloaded in the Installation section above. In this folder there is a file called environment.yml. This contains all the information we need to install the relevant packages.
Older versions of the conda package manager can be very slow, so we will install a new "solver" that speeds things up. To do this, from the Anaconda prompt (Windows) or in the terminal (OSx/Linux) you can run:
conda install -n base conda-libmamba-solver
Once the line above is run, to create out Python environment, we run:
conda env create -f environment.yml --solver=libmamba
This will create a Python environment called nero_winter_school. The environment won't be activated by default. To activate it, run:
conda activate nero_winter_school
Now you are ready to go!
Note: remember that you may need to reactivate the environment in every new window instance
Note: if you get a warning that "solver" is not a valid conda argument, you can
skip the libmamba install and run: conda env create -f environment.yml
Note: as you need to install libmamba solver in the conda base environment, this may not always be possible on cloud systems.
This module is based around a series of Jupyter Notebooks, designed to be run in Jupyter Lab.
Jupyter Notebooks support high-level interactive learning by allowing us to combine code, text description and data
visualisations. If you have not worked with Jupyter Notebooks before, please look at the Introduction to Python
and Project Jupyter module to get a short introduction to
their usage and benefits.
To run Jupyter Notebook, open a terminal or Anaconda prompt and make sure you have activated the correct environment. Again, navigate to the repository folder. Now you can run Jupyter using:
jupyter lab or jupyter-lab, depending on your operating system.
This should open Jupyter Lab in a browser window. On occasion, Jupyter may not be able to open a window and will give you a URL to past in your browser. Please do so, if required.
Note: Jupyter Lab is not able to find antyhing that is 'above' it in a directory tree, and you will unable to navigate to these. So make sure you run the line above from the correct directory!
Before you can run the notebooks, go to the credentials file and fill in with your passwords.
If you are running on a remote Jupyter Hub (e.g. WEkEO or Insula) you will need to perform some additional steps to ensure that you have the right python environment loaded in your notebook. When running locally, as long you have activated the correct environment, Jupyter will load it into your the "kernel" which runs your code by default. On cloud systems, we have to add the kernel to the system and apply it manually when we run.
To add an environment to a kernel you should first build the environment and activate it as described above. Once you have done this, you can add your environment to a kernel from the command line as follows:
python -m ipykernel install --name nero_winter_school --user
You should now be able to select the kernel from the menu bar in the top right hand side of any notebook you run.
Note: it sometimes takes a few seconds for the kernel to register in the notebook itself
Note: the above does not apply to Binder, which will load the environment supplied with the Git repository
If you would like to collaborate on a part of this code base or contribute to it please contact us on training@eumetsat.int. If you are have issues and need help, or you have found something that doesn't work, then please contact us at ops@eumetsat.int. We welcome your feedback!