DOI: 10.5281/zenodo.4088216
title: iSDAsoil: soil extractable Iron for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

description: iSDAsoil dataset soil extractable Iron (Fe) 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.fe_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron mean value, sol_log.fe_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron 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.fe_mehlich3 R-square: 0.817 Fitted values sd: 0.497 RMSE: 0.235 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.0165 -0.1312 -0.0082 0.1238 2.5077 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 3.913522 1.869721 2.093 0.036344 * regr.ranger 0.856893 0.007912 108.306 &lt; 2e-16 *** regr.xgboost 0.027856 0.007738 3.600 0.000318 *** regr.cubist 0.146095 0.007230 20.207 &lt; 2e-16 *** regr.nnet -0.879348 0.402810 -2.183 0.029037 * regr.cvglmnet 0.005610 0.004470 1.255 0.209415 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2349 on 57526 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.8173 F-statistic: 5.148e+04 on 5 and 57526 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>
description: iSDA is a social enterprise with the mission to improve smallholder farmer profitability across Africa. iSDA builds on the legacy of the African Soils information service (AfSIS) project. We are grateful for the outputs generated by all former AfSIS project partners: Columbia University, Rothamsted Research, World Agroforestry (ICRAF), Quantitative Engineering Design (QED), ISRIC — World Soil Information, International Institute of Tropical Agriculture (IITA), Ethiopia Soil Information Service (EthioSIS), Ghana Soil Information Service (GhaSIS), Nigeria Soil Information Service (NiSIS) and Tanzania Soil Information Service (TanSIS). More details on AfSIS partners and data contributors can be found at https://isda-africa.com/isdasoil
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