2020-09-21, 11:20–11:40, Room 1
Python libraries such as Sentinelsat, HARP and VISAN are very useful in order to download, analyze and visualize long series of remote sensing images. The objective of this talk is to expose how to use these tools to create a cartography map that reflects the mobility and industrial activity decrease during the COVID-19 lockdown.
The Covid-19 pandemic and its consequent lockdown has had a significant impact on industrial activity and traffic over many regions of the world. Both human activities are responsible for the NO2 emissions into the atmosphere, so its reduction also involved a reduction of this polluting gas.
Sentinel 5P -one of the satellites from the Sentinel constellation- carries on board the TROPOMI sensor. This sensor is prepared to measure many atmospheric gases, among which is NO2.
With a daily revisit period and a spatial resolution about 7km2, Sentinel 5p offers the possibility to accurately track the evolution of NO2 concentration over the world.
Sentinel 5p products are hosted on the Copernicus Open Acces Hub . The task to search and download the necessary images to analyze the NO2 evolution during a specific period of time/region can be automatized using the python library Sentinelsat.
A single day image from Sentinel-5p can not be significant enough to reflect the atmospheric NO2 registered values. Also, atmospheric conditions such as the presence of clouds can invalidate the obtained results. For these reasons, to analyze the evolution of NO2 gas is more realistic to work with averaged values (7 days, for example).
The calculation of these averaged values (and much more) can be done using the python library HARP.
Finally, the visualisation of the results can be done using VISAN. This python library provides powerful visualization functionalities for 2D plots and geospatial worldplots, which can be executed using Python.