WC(P)S demo of JKI Datacubes

Florian Beyer
ORCID: 0000-0002-9203-320X

Introduction

This repository demonstrates, how to query the Data Cubes provided by the JKI using Python and OGC web services WCS and WCPS.
The JKI datacube uses rasdaman enterprise in the background. The software is a database software specially designed for multidimensional large-scale raster data time series, which provides OGC web services (WMS, WCS and WCPS) as an interface.

All Datacubes have a spatial coverage of the area of Germany and come with spatial resolutions between 10 m and 1 km.

definitions

Data Cubes are a new paradigm for storing and analyzing geo raster (e.g. Earth observation) data. Datacubes, which are multi-dimensional arrays (often spatiotemporal), offer significant advantages over traditional data storage methods by simplifying access to and analysis of large datasets.

WCS OGC Web Coverage Service is a standard defined by the Open Geospatial Consortium (OGC) for serving geospatial data as coverages, which are digital representations of spatially continuous phenomena (e.g., satellite imagery, climate models, or terrain data). WCS provides interoperable access to such data, allowing users to query, extract, and retrieve data in formats suitable for analysis and visualization.

WCPS OGC Web Coverage Processing Service is an extension of the OGC Web Coverage Service (WCS) standard that enables advanced querying and processing of geospatial coverages through a declarative query language. It allows users to perform complex operationssuch as filtering, subsetting, arithmetic, or spatial-temporal analysisdirectly on large coverage datasets, without needing to download them first.

Features

The Juypter notebook demonstrates the following:

• How to access and and request multi-dimensional raster time series as data cubes
• How to get information of available Datcubes and their metadata
• Datacube 1 PHASE data: entry dates of phenological stages
• Datecube 2 EO data: earth observation data from Sentinel-2 are requested and a vegeation index called SAVi is calculated
• Datacube 3 Weather: daily precipitation sums (in mm) are requested
• A joint plot of all received data is printed
• verything is shown using a winter wheat field in Germany 2020.

Repository Structure

plaintext data/ # example geo vector files bavaria.geojson # example field in Bavaria (Germany) lower_saxony.geojson # example field in Lower Saxony (Germany) winterwheat2020.geojson # winter wheat field from 2020 in Lower Saxony (Germany) functions/ # required functions used in juypter notebook func_datacube_DWD.py # main function to query precipitation data cube func_datacube_PHASE.py # main function to query PHASE data cube func_datacube_S2_WCPS.py # main function to query Sentinel-2 data cube func_misc.py # additional functions used in the notebook CITATION.cff # plain text files with human- and machine-readable citation information for software codemeta.json # minimal metadata schema for science software and code credentials.py # credential files to get acces to restricted data cubes DemoPhaseWCS.ipynb # jupyter notebook and main file for demonstration requirements.txt # required python packages DATA_LICENSE.txt # license file for data files LICENSE # license file for code README.md # project description

credentials.py

If you have access to restricted data cubes (such as the Sentinel-2 datacube, showed in the Jupyter Notebook), it is recommended add your credentials in the credentials.py on your local machine.

codemeta.json, CITATION.cff & licensing

The Repository is also used to show best practices on "How to publish scientific code?" in order to fulfil the FAIR principles of Wilkinson et al. 2016 The FAIR principles emphasize making data Findable, Accessible, Interoperable, and Reusable, ensuring its long-term value for research and collaboration.

Therefore the repository is also published on zenodo as described HERE

• codemeta.json --> metadata schema for science software and code
• CITATION.cff --> citation information for software

Python3 settings

• script was developed with python 3.12.1
• geopandas, rasterio (geodata packages)
• xmltodict, tqdm
• ipyleaflet (for interactive map vizualisation in Juypter Notebook)
• all packages: see requirements.txt for our python environment

Conda Info (selected): conda version : 24.1.2 conda-build version : 24.1.1 python version : 3.11.5.final.0 solver : libmamba (default) virtual packages : __archspec=1=haswell __conda=24.1.2=0 __glibc=2.35=0 __linux=5.15.0=0 __unix=0=0 channel URLs : https://repo.anaconda.com/pkgs/main/linux-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/linux-64 https://repo.anaconda.com/pkgs/r/noarch platform : linux-64 user-agent : conda/24.1.2 requests/2.31.0 CPython/3.11.5 Linux/5.15.0-89-generic ubuntu/22.04.3 glibc/2.35 solver/libmamba conda-libmamba-solver/24.1.0 libmambapy/1.5.6 aau/0.4.2 UID:GID : 1002:1003 netrc file : None offline mode : False

Project supported and granted

www.fairagro.net

Licensing

This repository contains both code and data. They are licensed separately:

• Code are every files ending with *.py and *.ipynb: Licensed under the MIT License. See LICENSE for details.
• Data are all other files: Licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). See DATA_LICENSE.txt for details.