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soilhealthdatacube

Soil Health Data Cube for Europe

https://github.com/ai4soilhealth/soilhealthdatacube

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 11 DOI reference(s) in README
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    Academic publication links
    Links to: zenodo.org
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    Committers with academic emails
    1 of 4 committers (25.0%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (10.6%) to scientific vocabulary
Last synced: 7 months ago · JSON representation

Repository

Soil Health Data Cube for Europe

Basic Info
  • Host: GitHub
  • Owner: AI4SoilHealth
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 75.9 MB
Statistics
  • Stars: 13
  • Watchers: 2
  • Forks: 2
  • Open Issues: 1
  • Releases: 5
Created almost 3 years ago · Last pushed 8 months ago
Metadata Files
Readme License

README.md

Soil Health Data Cube

DOI

The Soil Health Data Cube for Europe provides technical documentation and computational notebooks to support soil health monitoring across Europe.

  • Data License: CC-BY (unless stated otherwise)
  • Code License: MIT License

For detailed technical information, see the Soil Health Data Cube for Europe Technical Manual.

Repository Contents

1. paneu_landmask

This folder contains files used to produce three Pan-EU land masks:

  1. Jupyter Notebook (Tile Products):

    • Land Mask: Differentiates land, ocean, and inland water
    • NUT-3 Code Map: Administrative areas at the NUT-3 level
    • ISO-3166 Country Code Map: Countries coded according to ISO-3166 standard

  2. Bash Scripts:

    • Merge tiles, reproject CRS, and resample to different resolutions

All land masks follow AI4SoilHealth Work Package 5 standards and align with data coverage from Copernicus Pan-European Land Service, closely matching the official EEA39 countries.

This landmask serves as a reference for landmask, spatial content, and resolution for all data products in this repository.

Contacts
- Xuemeng Tian
- Yu-Feng Ho
- Martijn Witjes

2. landsatbasedspectral_indices

A time-series of Landsat-based spectral indices (2000–2022) for continental Europe (including Ukraine, the UK, and Turkey).

  • Resolution: 30 meters
  • Temporal Coverage: Bi-monthly, annual, and long-term analyses
  • Applications:
    • Vegetation cover monitoring
    • Soil exposure assessment
    • Tillage and crop intensity analysis
    • Input for soil property modeling

Publication / Citation
Tian, X., Consoli, D., Witjes, M., Schneider, F., Parente, L., Şahin, M., Ho, Y.-F., Minařík, R., and Hengl, T. (2025):
Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022.
Earth Syst. Sci. Data, 17, 741–772. https://doi.org/10.5194/essd-17-741-2025

Indices Provided
- Vegetation: NDVI, SAVI, FAPAR
- Soil Exposure: Bare Soil Fraction (BSF)
- Tillage & Soil Sealing: NDTI, minNDTI
- Crop Patterns: Number of Seasons (NOS), Crop Duration Ratio (CDR)
- Water Dynamics: NDSI, NDWI

Production Workflow
General Workflow

Example
Bare Soil Fraction (%) time series for Europe (2000–2022):
BSF Time Series

Complete Access Catalog
Google Spreadsheet Catalog

3. SOCD_map

Contains notebooks and scripts for predictive modeling of soil organic carbon density (SOCD):

  • Notebooks (001–009): Testing various steps in the predictive modeling workflow
  • Benchmark Pipeline Script: benchmark_pipeline.py automates model building
  • Property-Specific Modeling (010–011): Loops pipeline across soil properties
  • Prediction Interval Models (012–014): Adds uncertainty quantification

Publication / Citation
Tian, X., de Bruin, S., Simoes, R., Isik, M.S., Minarik, R., Ho, Y., Şahin, M., Herold, M., Consoli, D., and Hengl, T. (2025):
Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning.
PeerJ, 13:e19605. https://doi.org/10.7717/peerj.19605

4. soilpropertymodel_pipeline

Implements the tested pipeline from SOCD_map to predict 10 key soil properties, with the resulting maps available at https://ecodatacube.eu.

5. WRB_map

Scripts to test, train, and evaluate predictive models for mapping soil types based on the IUSS World Reference Base (WRB) classification.

Acknowledgments & Funding

This work is part of the AI4SoilHealth project, funded by the European Union's Horizon Europe Research and Innovation Programme under Grant Agreement No. 101086179.

Funded by the European Union. The views expressed are those of the authors and do not necessarily reflect those of the European Union or the European Research Executive Agency.

Owner

  • Name: AI4SoilHealth Horizon Europe project
  • Login: AI4SoilHealth
  • Kind: organization
  • Email: tom.hengl@opengeohub.org
  • Location: Netherlands

Horizon Europe Grant Agreement No. 101086179. 2022–2026

GitHub Events

Total
  • Create event: 2
  • Issues event: 3
  • Release event: 2
  • Watch event: 7
  • Issue comment event: 2
  • Push event: 21
  • Fork event: 1
Last Year
  • Create event: 2
  • Issues event: 3
  • Release event: 2
  • Watch event: 7
  • Issue comment event: 2
  • Push event: 21
  • Fork event: 1

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 75
  • Total Committers: 4
  • Avg Commits per committer: 18.75
  • Development Distribution Score (DDS): 0.107
Past Year
  • Commits: 35
  • Committers: 2
  • Avg Commits per committer: 17.5
  • Development Distribution Score (DDS): 0.086
Top Committers
Name Email Commits
Meng2077 9****7 67
Tomislav Hengl t****l@g****m 5
yu-feng-ho 1****o 2
Davide Consoli d****i@o****g 1
Committer Domains (Top 20 + Academic)
opengeohub.org: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Meng2077 (1)
Pull Request Authors
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