Our Workflow
  •  Data Cleaning and harmonization: Different datasets were collected using different protocols, and therefore require bringing under a common format. Any erroneous datapoints or outliers are also removed. Some data is log scaled. 
  • Data loaded into a database uder a consistent format
  • Covariates prepared: Cloud-free Sentinel 2, Landsat and MODIS satellite images prepared for Africa. Other climactic and terrain variables were also used. 30m and 250m resolution covariates were prepared.
  • Preparation of models: model fine-tuning and feature selection, prepared for each soil property
  • Model running: An ensemble of 5 regression modelling algorithms was used to predict soil properties: Random Forest, Gradient Boosting, Cubist, Neural networks, Generalised Linear Modelling with Lasso or Elasticnet regularization 
  • Predictions at coarse (250m) and fine (30m) resolution: Created independently and then merged using ensembling
  • Properties are predicted at 0cm, 20cm and 50cm: Properties are then aggregated to 2 standards depths of 0-20 cm and 20-50cm
  • Quality control : Predictions undergo review by soil science experts, feedback and improvements made
  • Repeat:  Based upon feedback with experts, the entire process is tweaked and rerun. During the generation of the iSDAsoil maps, this process was run at least 5 times 
How to access iSDAsoil
Want to build an application using iSDAsoil or have data you would like to contribute? Get in touch!
Video Presentation by Jonathan Crouch, iSDA CEO
Introducing iSDAsoil...
  • We have predicted soil properties for Africa at 30m resolution, for 20 soil properties at 2 depths (0-20cm and 20-50cm)
  • 30m resolution equates to ~24 billion locations predicted across Africa per soil property
  • Soil property predictions were made using ensemble machine learning, incorporating high resolution satellite information
Use cases
  • Fertiliser recommendations
  • Yield forecasting
  • Crop suitability mapping
  • Carbon monitoring



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