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Analysis of Big Earth Data with Jupyter Notebooks

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Analysis of Big Earth Data with Jupyter Notebooks
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27
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production Year2020
Production PlaceWicc, Wageningen International Congress Centre B.V.

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Abstract
Growing volumes of Big Earth Data force us to change the way how we access and process large volumes of geospatial data. New (cloud-based) data systems are being developed, each offering different functionalities for users. This lecture is split in two parts: (i) (Cloud-based) data access systems This part will highlight five data access systems that allow you to access, download or process large volumes of Copernicus data related to climate and atmosphere. For each data system, an example is given how data can be retrieved. Data access systems that will be covered: - Copernicus Climate Data Store (CDS) / Copernicus Atmosphere Data Store (ADS) - WEkEO - Copernicus Data and Information Access System - Open Data Registry on Amazon Web Services - Google Earth Engine (ii) Case study: Analysis of Covid-19 with Sentinel-5P data This example showcases a case study analysing daily Sentinel-5P data from 2019 and 2020 with Jupyter notebooks and the Python library xarray in order to analyse a potential observed Covid-19 impact in 2020.