Description

iSDAsoil is a resource containing soil property predictions for the entire African continent, generated using machine learning. Maps for over 20 different soil properties have been created at 2 different depths (0-20 and 20-50cm). Soil property predictions were made using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples. Included in this dataset are images of predicted soil properties, model error and satellite covariates used in the mapping process.

Update Frequency

Based upon the availability of new data

License

CC-BY 4.0

Documentation

https://www.isda-africa.com/isdasoil/isdasoil-on-aws/

Managed By

Innovative Solutions for Decision Agriculture (iSDA)

See all datasets managed by Innovative Solutions for Decision Agriculture (iSDA).

Contact

info@isda-africa.com

How to Cite

iSDAsoil was accessed on DATE from https://registry.opendata.aws/isdasoil.

Usage Examples

Tutorials

• iSDAsoil Python tutorial by Matt Miller
  CloudFormationSageMaker

Tools & Applications

• iSDAsoil homepage - view soil property maps online by iSDA
  
• iSDAsoil liming demo app on Observable by Jamie Collinson
  

Publications

• African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning by Tomislav Hengl, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijević, Luka Glušica, Achim Dobermann, Stephan M. Haefele, Steve P. McGrath, Gifty E. Acquah, Jamie Collinson, Leandro Parente, Mohammadreza Sheykhmousa, Kazuki Saito, Jean-Martial Johnson, Jordan Chamberlin, Francis B. T. Silatsa, Martin Yemefack, John Wendt, Robert A. MacMillan, Ichsani Wheeler & Jonathan Crouch