Sentinel-5P is a satellite, designed by the European Space Agency (ESA) and funded by the European Commission’s Copernicus Programme, that orbits the Earth continuously observing air quality. The impact of air quality on human health and the environment is an important global issue. Currently, Sentinel-5P is the premier air quality observing satellite. It provides a macro level view of conditions, which helps us to better understand the characteristics of air quality events, such as:
- The geographical extent of pollution. Since sensors on the ground are sparse, observations from space improve our understanding of the spatial expanse of a polluted parcel of air.
- The impact of weather on air pollution at a large geographical scale, for example winds may just move poor quality from one location to another.
- The impact of landscape on air quality. For example the Alps at the top of Italy can trap air causing a build up of air pollution in this region.
As part of its ADAM Platform project , MEEO has created an archive of Sentinel-5P Level 2 data on the Registry of Open Data on AWS (RODA). This Open Data is available to anyone. In this tutorial, we give an example of how anyone can access the data, download a file and visualise the contents of the file.
MEEO has made several key products from the Sentinel-5P programme available on RODA. A full description of the contents of the data archive can be found here. In this tutorial we are going to look at carbon monoxide (CO) concentrations in the troposphere (bottom section of atmosphere).
The Sentinel-5P stored on Amazon is contained in a bucket (a container for data) which can be browsed here.
This tutorial will show how to use Python to visualise the data. We are using a Jupyter notebook to present the tutorial.
- Python 3 and Jupyter Notebook (Anaconda is a convenient distribution).
- Python Libraries: gdal, cartopy, rasterio and netcdf4.
If you do install Anaconda, open a cmd box and type etc.
- ‘conda install -c conda-forge gdal’
- ‘conda install -c conda-forge cartopy’
- ‘conda install -c conda-forge rasterio’
- ‘conda install -c conda-forge netcdf4’
This may take some time, depending on the speed of your computer and Internet connection.
The catalogue and getting the first file
We have provided a catalogue of the bucket that is available here.
Navigate the bucket by clicking on the links. Here is the path for the beginning of this example.
- Cloud Optimized GeoTIFF (COG) –>
- Offline Processing Stream –>
- Carbon Monoxide (CO) total column –>
- 2020 –>
- 01 –>
- 02 –>
- S5P_OFFL_L2__CO_____20200102T035646_20200102T053816_11503_01_010302_20200103T174817 –> carbonmonoxide_total_column
Following this path will lead to downloading this file:
Store this file on your local computer. It is an image file of the carbon monoxide observations for the 2nd of January 2020. In this image, the Sentinel-5P passed over the eastern seaboard of Australia.
Viewing the carbon monoxide from the bush fires in Australia using the COG format
In this section, we visualise the data in the COG format file that you have just downloaded and stored locally on your computer. To do this you will need to open a Jupyter Notebook and copy into it the code contained in each of the boxes below.
Air quality was an important public health issue in 2019 and is continuing to be so in 2020. We hope that this new archive will encourage collaboration within the Earth Observation community and drive new insights into Air Quality problems.
You can receive updates on the the data archive by emailing here: email@example.com
If you an want to learn more about ADAM what we do at MEEO, please contact us: firstname.lastname@example.org .