Summary
This study presents a continental-scale soil information system for Africa at 30 m spatial resolution, synthesising approximately 150,000 field soil samples with satellite Earth observation data and machine learning to predict 21 soil properties and classes at three depths. Using a two-scale 3D ensemble machine learning framework, the authors achieved varying accuracy levels across properties, with soil pH showing excellent predictability (CCC = 0.900) whilst extractable phosphorus and sulphur proved more challenging (CCC = 0.654–0.708). The resulting publicly available maps represent a substantial advance in soil data availability for the continent and identify climatic variables and Sentinel-2/Landsat spectral bands as the most important predictive covariates.
UK applicability
This study is primarily applicable to African soil mapping and agricultural planning. The methodological approach and machine learning framework could potentially be adapted for UK soil mapping, though the covariate importance (particularly climate) and sample density differ markedly from UK conditions, and UK soils are already characterised by existing national databases such as the National Soil Inventory.
Key measures
Soil pH, organic carbon, total nitrogen, total carbon, effective cation exchange capacity, extractable phosphorus, potassium, calcium, magnesium, sulphur, sodium, iron, zinc, silt, clay, sand, stone content, bulk density, and depth to bedrock at 0, 20, and 50 cm depths; model accuracy expressed as concordance correlation coefficient (CCC); spatial cross-validation performance
Outcomes reported
The study produced 30 m resolution maps of 21 soil properties and classes across Africa, including pH, organic carbon, nitrogen, micronutrients (zinc, iron, etc.), texture, and bulk density at three depths. Predictions were generated using ~150,000 soil samples and Earth observation data, with accuracy varying from soil pH (CCC = 0.900) to extractable phosphorus (CCC = 0.654).
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