https://github.com/eea/clms-hrvpp-tools-python

Tools for using high-resolution vegetation phenology and productivity (HR-VPP) data from Copernicus Land Service
https://github.com/eea/clms-hrvpp-tools-python

Last synced: 9 months ago
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Tools for using high-resolution vegetation phenology and productivity (HR-VPP) data from Copernicus Land Service

• Host: GitHub
• URL: https://github.com/eea/clms-hrvpp-tools-python
• Owner: eea
• License: mit
• Created: 2022-11-02T16:34:29.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-05-03T11:17:39.000Z (about 2 years ago)
• Last Synced: 2025-03-25T04:42:07.900Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 56.7 MB
• Stars: 10
• Watchers: 8
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# clms-hrvpp-tools-python

Python code and notebooks for using high-resolution vegetation phenology and productivity (HR-VPP) data from Copernicus Land Service.

## Data download

HR-VPP data can be downloaded using the WekEO portal and the WekEO HDA (Harmonzied Data Access) API. [This notebook](https://github.com/eea/clms-hrvpp-tools-python/blob/main/HRVPP_hda_demo/HRVPP%20hda%20demo.ipynb) shows how to use the WekEO HDA.

Next to that, an OpenSearch service is available to query the available HR-VPP datasets. When running on the CreoDIAS (CloudFerro) cloud, the data can be downloaded from the 'HRVPP' S3 bucket as shown in [this notebook](https://github.com/eea/clms-hrvpp-tools-python/blob/main/HRVPP_opensearch_demo/HRVPP%20overlay%20bucket%20demo.ipynb).