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Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa ...
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Abstract:
Link to scientific publication: https://doi.org/10.1016/j.scitotenv.2021.145384 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. 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 ...
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Keyword:
carbon sequestration; carbon stocks; land degradation; natural climate solutions; remote sensing; soil carbon; soil mapping; spatial prediction
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URL: https://dx.doi.org/10.5281/zenodo.4384691
https://zenodo.org/record/4384691
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Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa ...
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