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| Auteur principal: | |
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| Format: | Recurso digital |
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| Publié: |
Zenodo
2026
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| Accès en ligne: | https://doi.org/10.5281/zenodo.18706448 |
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Table des matières:
- <p>This release provides the computational framework used to assess Soil Organic Carbon (SOC) change detectability using repeated LUCAS topsoil observations, Earth Observation data, and machine learning. It includes all scripts and indexed analytical notebooks required to reproduce the modelling, uncertainty assessment, signal-to-noise ratio (SNR) diagnostics, and spatial aggregation analyses reported in the manuscript.</p> <p>Indexed notebooks (with numeric prefixes) define the formal execution sequence of the analytical pipeline used to generate manuscript results. Non-indexed notebooks are retained for exploratory analyses and methodological diagnostics and are not used in the final reported outcomes. Raw SOC observation data (LUCAS Topsoil Survey, Parcelas COS/INES, BZE-LW) and environmental covariates used in the modelling workflow are specified with open-access download links, with corresponding version identifiers documented in the covariate list.</p>