Name: iSDAsoil
License: CC-BY 4.0

agriculture analytics biodiversity conservation deep learning food security geospatial machine learning satellite imagery

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

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

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

Resources on AWS

Description

iSDAsoil data and covariates

Resource type

S3 Bucket

Amazon Resource Name (ARN)

arn:aws:s3:::isdasoil

AWS Region

us-west-2

AWS CLI Access (No AWS account required)

aws s3 ls --no-sign-request s3://isdasoil/

Explore

Browse data using the STAC viewer

iSDAsoil