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

iSDAsoil dataset soil extractable Potassium (K) 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.k_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium mean value,
• sol_log.k_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium 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.k_mehlich3 
R-square: 0.773 
Fitted values sd: 0.938 
RMSE: 0.509 

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

Residuals:
    Min      1Q  Median      3Q     Max 
-4.3088 -0.2648 -0.0037  0.2639  6.8136 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)   10.907726   6.134422   1.778   0.0754 .  
regr.ranger    1.004487   0.003878 259.026   <2e-16 ***
regr.xgboost  -0.004081   0.004739  -0.861   0.3892    
regr.cubist    0.084556   0.004346  19.454   <2e-16 ***
regr.nnet     -2.205286   1.228586  -1.795   0.0727 .  
regr.cvglmnet -0.064510   0.003933 -16.401   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5092 on 139122 degrees of freedom
Multiple R-squared:  0.7725,	Adjusted R-squared:  0.7725 
F-statistic: 9.451e+04 on 5 and 139122 DF,  p-value: < 2.2e-16

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

ppm = expm1( y / 10 )

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