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Published October 14, 2020 | Version v0.13
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iSDAsoil: soil extractable Al for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

Authors/Creators

  • 1. EnvirometriX
  • 2. iSDA Africa
  • 3. MultiOne
  • 4. University of Belgrade

Description

iSDAsoil dataset soil extractable Aluminium 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, LandPKS, and other national and regional soil datasets). Layer description:

  • sol_db_od_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium mean value,
  • sol_db_od_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium 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: log.al_mehlich3 
R-square: 0.881 
Fitted values sd: 0.872 
RMSE: 0.321 

Random forest model:
Call:
stats::lm(formula = f, data = d)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.7042 -0.1036  0.0059  0.1189  3.3777 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)   -0.675492   2.771906  -0.244    0.807    
regr.ranger    0.879567   0.005464 160.969   <2e-16 ***
regr.xgboost   0.071537   0.005813  12.306   <2e-16 ***
regr.cubist    0.150157   0.004553  32.979   <2e-16 ***
regr.nnet      0.087603   0.431261   0.203    0.839    
regr.cvglmnet -0.084440   0.003182 -26.534   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3208 on 63551 degrees of freedom
Multiple R-squared:  0.8808,	Adjusted R-squared:  0.8808 
F-statistic: 9.391e+04 on 5 and 63551 DF,  p-value: < 2.2e-16

To back-transform values (y) to ppm use the following formula:

ppm = expm1( y / 10 )

To submit an issue or request support please visit https://isda-africa.com/isdasoil

Notes

iSDA is a social enterprise founded by – Rothamsted Research, the World Agroforestry Centre (ICRAF) and the International Institute of Tropical Agriculture (IITA) – building on the legacy of the AfSIS project to create financially sustainable agronomy solutions for smallholder farmers. We are grateful to all national soil agencies especially GhaSIS, TanSIS, EthioSIS and NiSIS for providing soil sampling data and technical support.

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Additional details

References

  • Hengl, T., Leenaars, J. G., Shepherd, K. D., Walsh, M. G., Heuvelink, G. B., Mamo, T., ... & Wheeler, I. (2017). Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutrient Cycling in Agroecosystems, 109(1), 77-102.
  • Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.
  • Leenaars, J. G. B. (2014). Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa (with dataset). Africa Soil Information Service (AfSIS) project (No. 2014/03). ISRIC-World Soil Information.