Soil Organic Carbon Stocks & Trends South Africa¶
Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details
Venter, Zander S., Heidi-Jayne Hawkins, Michael D. Cramer, and Anthony J. Mills. "Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa." Science of The Total Environment 771 (2021): 145384.
Earth Engine Snippet¶
var SOC30_mean = ee.ImageCollection("projects/sat-io/open-datasets/NINA/SOC30_SA_mean"); var SOC30_trend = ee.ImageCollection("projects/sat-io/open-datasets/NINA/SOC30_SA_trend");
Sample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/SOIL-ORGANIC-CARBON-SA
Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are:
- "SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2
- "SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years
NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.
Venter, Zander S, Hawkins, Heidi-Jayne, Cramer, Michael D, & Mills, Anthony J. (2020). Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa (Version 01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4384692
Creative Commons Attribution-Share Alike 4.0 International License
Created by: Venter, Zander S, Hawkins, Heidi-Jayne, Cramer, Michael D, & Mills, Anthony J
Curated by: Samapriya Roy
Keywords: : carbon stocks, land degradation, natural climate solutions, remote sensing, soil mapping, spatial prediction, soil carbon, carbon sequestration
Last updated: 2021-04-29