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

<p>iSDAsoil dataset soil stone content / coarse fragments log-transformed predicted at 30 m resolution for 0&ndash;20 and 20&ndash;50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>.&nbsp;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 (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J.&nbsp;<em>et al.</em>&nbsp;African soil properties and nutrients mapped at 30&nbsp;m spatial resolution using two-scale ensemble machine learning.&nbsp;<em>Sci Rep</em>&nbsp;<strong>11,&nbsp;</strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.wpg2_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil stone content mean value,</li> <li>sol_log.wpg2_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil stone content model (prediction) errors,</li> </ul> <p>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 (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>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(&gt;|t|) (Intercept) -0.008606 1.361982 -0.006 0.995 regr.ranger 0.972265 0.004443 218.854 &lt; 2e-16 *** regr.xgboost 0.034649 0.006404 5.411 6.3e-08 *** regr.cubist 0.069589 0.005229 13.308 &lt; 2e-16 *** regr.nnet -0.012756 0.796535 -0.016 0.987 regr.cvglmnet -0.056645 0.005509 -10.283 &lt; 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: &lt; 2.2e-16 </code></pre> <p>To back-transform values (y) to % use the following formula:</p> <pre><code>% = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>