Dalle Vaglie et al. (2026) HUMERIS Global Soil Dataset

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Identification

• Journal: Mendeley Data
• Year: 2026
• Date: 2026-09-15
• Authors: Dalle Vaglie, Matteo, Francini, Saverio, Chirici, Gherardo, martellozzo, federico
• DOI: 10.17632/z8v8m579z4

Research Groups

• ISRIC – World Soil Information
• OpenLandMap
• LUCAS (Land Use/Cover Area frame statistical survey)
• Google Earth Engine

Short Summary

This paper presents the HUMERIS Global Soil Properties Dataset, a comprehensive 39-year time series of global soil maps at 1 km resolution for four key soil properties. The dataset captures spatio-temporal dynamics using machine learning techniques and environmental predictors.

Objective

• Develop a large-scale framework for estimating soil carbon, nitrogen, pH, and salinity dynamics from 1985 to 2023

Study Configuration

• Spatial Scale: Global (1 km resolution)
• Temporal Scale: 39 years (1985–2023)

Methodology and Data

• Models used: Machine learning techniques with 41 environmental predictors
• Data sources:
  • ISRIC-WISE dataset
  • OpenLandMap dataset
  • LUCAS dataset
  • Remotely sensed vegetation indices
  • Climatic variables
  • Topographic indices
  • Land cover data

Main Results

• The HUMERIS Global Soil Properties Dataset provides annual global soil maps for topsoil (0–30 cm) covering pH, soil salinity (ECe), nitrogen (N), and organic carbon (OC)
• Advanced machine learning techniques capture spatio-temporal dynamics of soil properties with high accuracy

Contributions

• Original contribution to the field of soil science by providing a comprehensive dataset for large-scale estimation of soil properties
• Enables users to assess overall state and variability of soil health indicators across different regions

Funding

• Not specified in the provided text

Citation

@article{DalleVaglie2026HUMERIS,
  author = {Dalle Vaglie, Matteo and Francini, Saverio and Chirici, Gherardo and martellozzo, federico},
  title = {HUMERIS Global Soil Dataset},
  journal = {Mendeley Data},
  year = {2026},
  doi = {10.17632/z8v8m579z4},
  url = {https://doi.org/10.17632/z8v8m579z4}
}