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

iSDAsoil dataset soil pH (1:1 Soil-Water Suspension) 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). Layer description:

sol_ph_h2o_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil pH mean value,

sol_ph_h2o_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil pH 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: ph_h2o
R-square: 0.818
Fitted values sd: 0.972
RMSE: 0.459

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

Residuals:
Min 1Q Median 3Q Max
-5.5939 -0.2328 -0.0066 0.2222 4.7477

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.113440 1.164473 0.956 0.338986
regr.ranger 1.032918 0.003138 329.116 < 2e-16 ***
regr.xgboost -0.014201 0.004185 -3.393 0.000691 ***
regr.cubist 0.049667 0.003709 13.392 < 2e-16 ***
regr.nnet -0.188570 0.188214 -1.002 0.316398
regr.cvglmnet -0.059763 0.003636 -16.438 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4591 on 133378 degrees of freedom
Multiple R-squared: 0.8176, Adjusted R-squared: 0.8176
F-statistic: 1.195e+05 on 5 and 133378 DF, p-value: < 2.2e-16

To back-transform values (y) to index use:

index = y / 10

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