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/s41598-021-85639-y

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

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/isdasoil