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

iSDAsoil dataset soil extractable Phosphorus (P) 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.p_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Phosphorus mean value, sol_log.p_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Phosphorus 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.p_mehlich3 R-square: 0.486 Fitted values sd: 0.687 RMSE: 0.707 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.2892 -0.3942 -0.0637 0.2614 4.9466 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 3.378801 3.143200 1.075 0.282 regr.ranger 0.861655 0.011099 77.631 &lt; 2e-16 *** regr.xgboost 0.066139 0.013091 5.052 4.38e-07 *** regr.cubist 0.157674 0.008886 17.744 &lt; 2e-16 *** regr.nnet -1.649621 1.442240 -1.144 0.253 regr.cvglmnet 0.013628 0.010407 1.310 0.190 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7066 on 53493 degrees of freedom Multiple R-squared: 0.486, Adjusted R-squared: 0.486 F-statistic: 1.012e+04 on 5 and 53493 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>iSDAsoil 数据集为针对0–20cm与20–50cm土层深度区间、以30米分辨率生成的对数变换后土壤有效磷（extractable Phosphorus, P）预测数据。该数据采用WGS84坐标系投影，存储为云优化GeoTIFF（Cloud-Optimized GeoTIFF，简称COG）格式。预测结果通过多尺度集成机器学习（Ensemble Machine Learning）生成，协变量包含250米分辨率（如MODIS、PROBA-V、气候变量等）与30米分辨率（如数字地形模型衍生数据、Landsat、Sentinel-2等）的数据集。 模型训练采用泛非地区土壤样本与剖面数据集，包括iSDA点位、AfSPDB及其他国家与区域土壤数据集。 引用格式：Hengl, T., Miller, M.A.E., Križan, J. 等人（<em>et al.</em>）. 基于双尺度集成机器学习的30米空间分辨率非洲土壤属性与养分制图. 《科学报告》（Sci Rep）, 第11卷, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y 若需在QGIS中打开该地图并直接进行计算，请使用**云优化GeoTIFF版本**。 图层说明： - sol_log.p_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif：预测的土壤有效磷均值 - sol_log.p_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif：预测的土壤有效磷模型（预测）误差。模型误差通过自助法（bootstrapping）推导得到：md为5折空间交叉验证（spatial blocking）中各个体学习器的标准差。 针对该变量的5折交叉验证（采用mlr::makeStackedLearner实现）结果如下： <pre><code>Variable: log.p_mehlich3 R-square: 0.486 Fitted values sd: 0.687 RMSE: 0.707 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.2892 -0.3942 -0.0637 0.2614 4.9466 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 3.378801 3.143200 1.075 0.282 regr.ranger 0.861655 0.011099 77.631 &lt; 2e-16 *** regr.xgboost 0.066139 0.013091 5.052 4.38e-07 *** regr.cubist 0.157674 0.008886 17.744 &lt; 2e-16 *** regr.nnet -1.649621 1.442240 -1.144 0.253 regr.cvglmnet 0.013628 0.010407 1.310 0.190 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7066 on 53493 degrees of freedom Multiple R-squared: 0.486, Adjusted R-squared: 0.486 F-statistic: 1.012e+04 on 5 and 53493 DF, p-value: &lt; 2.2e-16 </code></pre> 若需将预测值（y）还原为百万分比浓度（ppm），可使用以下公式： <pre><code>ppm = expm1( y / 10 )</code></pre> 若需提交问题或请求支持，请访问 https://isda-africa.com/isdasoil