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

<p>iSDAsoil dataset soil&nbsp;total carbon in permilles (g/kg) 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.c_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon mean value,</li> <li>sol_log.c_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon 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.c_tot R-square: 0.794 Fitted values sd: 0.571 RMSE: 0.291 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.70312 -0.16714 -0.00549 0.15691 3.01116 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 0.025841 0.032713 0.790 0.429570 regr.ranger 0.902240 0.008462 106.619 &lt; 2e-16 *** regr.xgboost 0.066535 0.008145 8.169 3.18e-16 *** regr.cubist 0.145730 0.006927 21.039 &lt; 2e-16 *** regr.nnet -0.048957 0.013466 -3.636 0.000278 *** regr.cvglmnet -0.075212 0.005556 -13.537 &lt; 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.291 on 50140 degrees of freedom Multiple R-squared: 0.7938, Adjusted R-squared: 0.7938 F-statistic: 3.861e+04 on 5 and 50140 DF, p-value: &lt; 2.2e-16 </code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = 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>