iSDAsoil: soil stone content for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil stone content / coarse fragments log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:

Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41…

To open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF ver….

Layer description:

• sol_log.wpg2_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil stone content mean value,
• sol_log.wpg2_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil stone content model (prediction) errors,

Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (mlr::makeStackedLearner) for this variable indicates:

Variable: log.wpg2 
R-square: 0.709 
Fitted values sd: 1.25 
RMSE: 0.803 

Random forest model:
Call:
stats::lm(formula = f, data = d)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0555 -0.3113 -0.0222  0.2378  4.5794 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)   -0.008606   1.361982  -0.006    0.995    
regr.ranger    0.972265   0.004443 218.854  < 2e-16 ***
regr.xgboost   0.034649   0.006404   5.411  6.3e-08 ***
regr.cubist    0.069589   0.005229  13.308  < 2e-16 ***
regr.nnet     -0.012756   0.796535  -0.016    0.987    
regr.cvglmnet -0.056645   0.005509 -10.283  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.8032 on 92785 degrees of freedom
Multiple R-squared:  0.7092,	Adjusted R-squared:  0.7092 
F-statistic: 4.525e+04 on 5 and 92785 DF,  p-value: < 2.2e-16

To back-transform values (y) to % use the following formula:

% = expm1( y / 10 )

To submit an issue or request support please visit https://isda-africa.com/isd…

iSDA is a social enterprise with the mission to improve smallholder farmer profitability across Africa. iSDA builds on the legacy of the African Soils information service (AfSIS) project. We are grateful for the outputs generated by all former AfSIS project partners: Columbia University, Rothamsted Research, World Agroforestry (ICRAF), Quantitative Engineering Design (QED), ISRIC — World Soil Information, International Institute of Tropical Agriculture (IITA), Ethiopia Soil Information Service (EthioSIS), Ghana Soil Information Service (GhaSIS), Nigeria Soil Information Service (NiSIS) and Tanzania Soil Information Service (TanSIS). More details on AfSIS partners and data contributors can be found at isda-africa.com