GLOBAL COASTAL WETLANDS LAB
Published December 8, 2025 | Version v2
Dataset Open

Global soil organic carbon in tidal marshes version 2

Authors/Creators

  • 1. University of Cambridge
  • 2. ROR icon Université Paris-Saclay
  • 3. ROR icon The Nature Conservancy
  • 4. ROR icon Tulane University
  • 5. ROR icon James Cook University
  • 6. ROR icon University of Wollongong
  • 7. ROR icon U.S. Army Engineer Research and Development Center
  • 8. ROR icon Louisiana State University
  • 9. ROR icon North Carolina State University
  • 10. ROR icon European Molecular Biology Laboratory
  • 11. ROR icon Griffith University
  • 12. ROR icon Nelson Mandela University
  • 13. School of Geography and Sustainable Development, University of St Andrews, St Andrews, UK
  • 14. Scottish Association of Marine Science, Oban, UK
  • 15. ROR icon Universidade Federal do Rio Grande
  • 16. Brazilian Network of Climate Change Studies - Rede CLIMA
  • 17. ROR icon University College Dublin
  • 18. ROR icon Deakin University
  • 19. ROR icon Smithsonian Environmental Research Center
  • 20. ROR icon Swansea University
  • 21. ROR icon Bangor University
  • 22. ROR icon University of Queensland
  • 23. ROR icon University of Münster
  • 24. ROR icon Environmental Defense Fund
  • 25. Anthesis South Africa
  • 26. ROR icon Universidad Nacional Autónoma de México
  • 27. ROR icon Centre d'Estudis Avançats de Blanes
  • 28. ROR icon University of St Andrews
  • 29. Swiss Federal Institute of Technology (ETH Zürich)
  • 30. U.S. Geological Survey

Description

This dataset is the second version of the predictions, expected model error, and area of applicability of the global soil organic carbon in tidal marshes at a 30 m resolution. All methods are provided in detail in the accompanying Nature Communications paper, Maxwell et al. (2024) Soil carbon in the world's tidal marshes. This is a correction to version 1 (where values were integers instead of floats and maxed out to 256 due to a data formatting error when preparing the tiles for the Zenodo upload).

Tidal marsh extent map

  • Worthington et al. (2024) The distribution of global tidal marshes from Earth observation data. Global Ecology and Biogeography. 

Training data

  • Maxwell et al. (2023) Global dataset of soil organic carbon in tidal marshes. Scientific Data.
  • Holmquist et al. (2024) The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy. Global Change Biology. 
  • Citations for the training data from the above-mentioned syntheses are available here.

Model 

  • Code available on Github.
  • 3D soil modelling approach: Hengl & MacMillan (2019). Predictive Soil Mapping with R.
  • Random forest model: Kuhn (2008). Building Predictive Models in R Using the caret Package. J. Stat. Softw. 
  • k-NNDM spatial cross validation: Meyer, Milà & Ludwig (2022). CAST: ‘caret’ Applications for Spatial-Temporal Models. 
  • Area of applicability: Meyer & Pebesma (2022). Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications.

Description of files

  • GRID.zip: shapefile with the location of each tile in the zipped folders below 
  • Final_predicted_SOC_both_layers.png: final predicted tidal marsh soil organic carbon (SOC) for a) the 0-30 cm soil layer and b) the 30-100 cm soil layer (aggregated per 2° cell). 

Area of applicability 

  • aoa0.zip: the area of applicability (AOA) mask for the 0-30 cm layer. Pixels with an AOA value of 0 or 0.5 are considered outside the AOA; with an AOA value of 1 are considered inside the AOA.
  • aoa30.zip: the area of applicability (AOA) mask for the 30-100 cm layer. Pixels with an AOA value of 0 or 0.5 are considered outside the AOA; with an AOA value of 1 are considered inside the AOA.

Final predictions and expected error 

  • pred0_aoa.zip: predicted soil organic carbon for the 0-30 cm layer (Mg C ha-1), masked by the area of applicability.
  • pred30_aoa.zip: predicted soil organic carbon for the 30-100 cm layer (Mg C ha-1), masked by the area of applicability.
  • err0_aoa.zip: expected model error for the 0-30 cm layer (Mg C ha-1), masked by the area of applicability. 
  • err30_aoa.zip: expected model error for the 30-100 cm layer (Mg C ha-1), masked by the area of applicability. 

Initial predictions and expected error

  • pred0.zip: predicted soil organic carbon for the 0-30 cm layer (Mg C ha-1).
  • pred30.zip: predicted soil organic carbon for the 30-100 cm layer (Mg C ha-1).
  • err0.zip: expected model error for the 0-30 cm layer for all tidal marsh extent pixels (Mg C ha-1).
  • err30.zip: expected model error for the 30-100 cm layer for all tidal marsh extent pixels (Mg C ha-1).

Files

Final_predicted_SOC_both_layers.png

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Additional details

Related works

Is described by
Journal: 10.1038/s41467-024-54572-9 (DOI)

Software

Repository URL
https://github.com/Tania-Maxwell/global-marshC-map