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Related Experiment Video

Updated: Mar 7, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

SoilGrids250m: Global gridded soil information based on machine learning.

Tomislav Hengl¹, Jorge Mendes de Jesus¹, Gerard B M Heuvelink¹

¹ISRIC - World Soil Information, Wageningen, the Netherlands.

|February 17, 2017

Summary

This summary is machine-generated.

The improved SoilGrids system now provides global soil property predictions at 250m resolution. This advanced system significantly enhances accuracy, offering valuable data for soil science research and applications.

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Area of Science:

Soil Science
Geospatial Analysis
Machine Learning Applications

Background:

Global soil mapping efforts are crucial for understanding soil resources.
Previous SoilGrids versions offered valuable but lower-resolution soil data.
Advancements in remote sensing and machine learning enable more detailed soil predictions.

Purpose of the Study:

To describe the development and accuracy of the enhanced SoilGrids system at 250m resolution.
To provide global predictions for key soil properties and classes.
To assess the improvements over the previous SoilGrids version.

Main Methods:

Utilized ca. 150,000 soil profiles for model training.
Employed 158 remote sensing-based covariates at finer resolutions.
Applied an ensemble of machine learning algorithms (random forest, gradient boosting, logistic regression).

Main Results:

Achieved 250m resolution global predictions for multiple soil properties and classes.
Explained 56-83% of variation in soil properties, with an average of 61%.
Demonstrated 60-230% relative accuracy improvement compared to the 1km SoilGrids version.

Conclusions:

The 250m SoilGrids system represents a significant advancement in global soil mapping.
Machine learning and improved covariates are key drivers of enhanced prediction accuracy.
Future work includes uncertainty assessment and multiscale data integration for more comprehensive soil information.