Tools
Tools
Maxwell, T., Spalding, M., Friess, D., Murray, N., Rogers, K., Rovai, A., Smart, L., Weilguny, L., Adame, M., Adams, J., Austin, W., Copertino, M., Cott, G., Duarte de Paula Costa, M., Holmquist, J., Ladd, C., Lovelock, C., Ludwig, M., Moritsch, M., Navarro, A., et al. (2024). Global soil organic carbon in tidal marshes version 1. 10.5281/zenodo.10940065.
Full text not available from this repository.Abstract
This dataset is the first 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.
Tidal marsh extent map
Worthington et al. (2023) The distribution of global tidal marshes from earth observation data. bioRxiv.
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).
| Item Type: | Data |
|---|---|
| Additional Information: | Creative Commons Attribution 4.0 International |
| Research Programs: | Biodiversity and Natural Resources (BNR) |
| Depositing User: | Luke Kirwan |
| Date Deposited: | 09 Jan 2025 13:59 |
| Last Modified: | 09 Jan 2025 13:59 |
| URI: | https://pure.iiasa.ac.at/20309 |
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