Multi‐Depth Soil Attributes Dataset

ABSTRACT Comprehensive soil data at multiple depths are essential for climate change assessments, biodiversity analyses, and land‐use monitoring. However, generating such information through laboratory analyses is costly and labor‐intensive. Existing global resources, such as SoilGrids and statistical packages like geodata , provide raster data for soil layers at 0–5 cm, 5–15 cm, and 15–30 cm, but many applications require alternative depth intervals—for example, 0–20 cm for carbon stock modelling (e.g., CENTURY) or 0–30 cm for ecosystem services (e.g., InVEST) and studies of plant growth, water availability, and pollutant dynamics. To address this gap, we developed a global database of raster soil data at depths of 0–10 cm, 0–15 cm, 0–20 cm, 0–25 cm, and 0–30 cm. Soil attributes at specific depths were computed through interpolation and weighted averaging of SoilGrids rasters and validated against empirical soil data from multiple continents, compiled from major global repositories and peer‐reviewed studies. Standard statistical procedures confirmed the robust accuracy of the interpolated rasters. The database provides values for bulk density (Mg m −3 ), soil texture (%), soil acidity (pH), total nitrogen (g kg −1 ), soil organic carbon (g kg −1 ), and soil organic carbon stock (Mg ha −1 ). Potential applications include (i) biogeochemical modelling of soil carbon, (ii) aboveground biomass modelling, (iii) species distribution modelling, (iv) biodiversity assessments, and (v) ecosystem diagnostics. The dataset is open access under a Creative Commons licence, adheres to FAIR (Findable, Accessible, Interoperable, and Reusable) principles, and is hosted on Zenodo due to its large size, links access: https://doi.org/10.5281/zenodo.14721139 . Users are kindly requested to cite this paper when employing the dataset.

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Publication Details

Journal
Geoscience Data Journal
Published
2026-01-01
DOI
https://doi.org/10.1002/gdj3.70052
Citations
1
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
21.83

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article

Multi‐Depth Soil Attributes Dataset

Sidney F. Gouveia, Eduardo Vinícius da Silva Oliveira, Wadson de Jesus Correia, Jhonatan Rafael Zárate‐Salazar
1 citations
Geoscience Data Journal
Soil Geostatistics and Mapping
21.83
article

Multi‐Depth Soil Attributes Dataset

Sidney F. Gouveia, Eduardo Vinícius da Silva Oliveira, Wadson de Jesus Correia, Jhonatan Rafael Zárate‐Salazar
article en
1 citations

Abstract

ABSTRACT Comprehensive soil data at multiple depths are essential for climate change assessments, biodiversity analyses, and land‐use monitoring. However, generating such information through laboratory analyses is costly and labor‐intensive. Existing global resources, such as SoilGrids and statistical packages like geodata , provide raster data for soil layers at 0–5 cm, 5–15 cm, and 15–30 cm, but many applications require alternative depth intervals—for example, 0–20 cm for carbon stock modelling (e.g., CENTURY) or 0–30 cm for ecosystem services (e.g., InVEST) and studies of plant growth, water availability, and pollutant dynamics. To address this gap, we developed a global database of raster soil data at depths of 0–10 cm, 0–15 cm, 0–20 cm, 0–25 cm, and 0–30 cm. Soil attributes at specific depths were computed through interpolation and weighted averaging of SoilGrids rasters and validated against empirical soil data from multiple continents, compiled from major global repositories and peer‐reviewed studies. Standard statistical procedures confirmed the robust accuracy of the interpolated rasters. The database provides values for bulk density (Mg m −3 ), soil texture (%), soil acidity (pH), total nitrogen (g kg −1 ), soil organic carbon (g kg −1 ), and soil organic carbon stock (Mg ha −1 ). Potential applications include (i) biogeochemical modelling of soil carbon, (ii) aboveground biomass modelling, (iii) species distribution modelling, (iv) biodiversity assessments, and (v) ecosystem diagnostics. The dataset is open access under a Creative Commons licence, adheres to FAIR (Findable, Accessible, Interoperable, and Reusable) principles, and is hosted on Zenodo due to its large size, links access: https://doi.org/10.5281/zenodo.14721139 . Users are kindly requested to cite this paper when employing the dataset.

Geoscience Data JournalVol. 13(1)
Universidade Federal de Sergipe (BR)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Goiás, Fundação de Apoio à Pesquisa e à Inovação Tecnológica do Estado de Sergipe
Openalex Percentile: Top 3%
Soil Geostatistics and Mapping
21.83
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