{ "@context": "http://schema.org", "@type": "Dataset", "@id": "https://doi.org/10.5281/zenodo.4087904", "url": "https://zenodo.org/record/4087904", "name": "iSDAsoil: soil fine-earth bulk density for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths", "author": [ { "name": "Tomislav Hengl", "givenName": "Tomislav", "familyName": "Hengl", "affiliation": { "@type": "Organization", "name": "EnvirometriX" }, "@id": "https://orcid.org/0000-0002-9921-5129" }, { "name": "Matt Miller", "givenName": "Matt", "familyName": "Miller", "affiliation": { "@type": "Organization", "name": "Innovative Solutions for Decision Agriculture Ltd (iSDA)" }, "@id": "https://orcid.org/0000-0002-1643-3076" }, { "name": "Josip Križan", "givenName": "Josip", "familyName": "Križan", "affiliation": { "@type": "Organization", "name": "MultiOne" }, "@id": "https://orcid.org/0000-0002-4557-3537" }, { "name": "Milan Kilibarda", "givenName": "Milan", "familyName": "Kilibarda", "affiliation": { "@type": "Organization", "name": "University of Belgrade" }, "@id": "https://orcid.org/0000-0002-2930-3596" }, { "name": "Gifty Acquah", "givenName": "Gifty", "familyName": "Acquah", "affiliation": { "@type": "Organization", "name": "Rothamsted Research" }, "@id": "https://orcid.org/0000-0003-4269-0903" }, { "name": "Andrew M. Sila", "givenName": "Andrew M.", "familyName": "Sila", "affiliation": { "@type": "Organization", "name": "World Agroforestry (ICRAF)" }, "@id": "https://orcid.org/0000-0002-3991-8770" } ], "description": "iSDAsoil dataset soil fine-earth bulk density in 10×kg/m3 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, LandPKS, and other national and regional soil datasets). Cite as: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 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 Cloud-Optimized GeoTIFF version. Layer description: sol_db_od_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density mean value, sol_db_od_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density 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:
Variable: db_od R-square: 0.819 Fitted values sd: 0.269 RMSE: 0.126 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.06778 -0.06450 0.00215 0.06585 0.90016 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.05538 0.04860 -1.140 0.25451 regr.ranger 0.86305 0.01577 54.733 < 2e-16 *** regr.xgboost 0.15383 0.01651 9.315 < 2e-16 *** regr.cubist 0.02039 0.01113 1.832 0.06695 . regr.nnet 0.03465 0.03710 0.934 0.35036 regr.cvglmnet -0.03021 0.01032 -2.927 0.00343 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1263 on 13565 degrees of freedom Multiple R-squared: 0.8194, Adjusted R-squared: 0.8193 F-statistic: 1.231e+04 on 5 and 13565 DF, p-value: < 2.2e-16
To back-transform values (y) to kg/m-cubic use: kg/m3 = y * 10
To submit an issue or request support please visit https://isda-africa.com/isdasoil",
"license": [
"https://creativecommons.org/licenses/by/4.0/legalcode",
"info:eu-repo/semantics/openAccess"
],
"version": "v0.13",
"keywords": "soil, Africa, bulk density, iSDA",
"inLanguage": "en",
"datePublished": "2020-10-14",
"schemaVersion": "http://datacite.org/schema/kernel-4",
"publisher": {
"@type": "Organization",
"name": "Zenodo"
},
"provider": {
"@type": "Organization",
"name": "datacite"
}
}