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

iSDAsoil dataset soil extractable Magnesium (Mg) 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. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>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 <strong>Cloud-Optimized GeoTIFF version</strong>. Layer description: sol_log.mg_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Magnesium mean value, sol_log.mg_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Magnesium 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: <pre><code>Variable: log.mg_mehlich3 R-square: 0.815 Fitted values sd: 1.05 RMSE: 0.498 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.8775 -0.2312 0.0028 0.2465 3.7400 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) -0.034349 0.051219 -0.671 0.5025 regr.ranger 1.034217 0.003263 316.950 &lt;2e-16 *** regr.xgboost -0.008057 0.003854 -2.091 0.0366 * regr.cubist 0.073223 0.003649 20.067 &lt;2e-16 *** regr.nnet -0.017388 0.009528 -1.825 0.0680 . regr.cvglmnet -0.075566 0.003402 -22.213 &lt;2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4979 on 136681 degrees of freedom Multiple R-squared: 0.8152, Adjusted R-squared: 0.8152 F-statistic: 1.206e+05 on 5 and 136681 DF, p-value: &lt; 2.2e-16 </code></pre> To back-transform values (y) to ppm use the following formula: <pre><code>ppm = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <strong>https://isda-africa.com/isdasoil</strong>