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Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning.

Flavio Piccoli¹, Mirko Paolo Barbato¹, Marco Peracchi¹

¹Department of Informatics, Systems and Communications, Università degli Studi di Milano-Bicocca, 20126 Milano, Italy.

Sensors (Basel, Switzerland)

|September 28, 2023

Summary

Machine learning accurately estimates European soil properties using Sentinel-3 satellite data. Integrating digital elevation model (DEM) derivatives significantly enhances soil texture and cation exchange capacity (CEC) predictions.

Keywords:

Sentinel-3 digital elevation model digital soil mapping machine learning multispectral sensing

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Area of Science:

Earth Science
Remote Sensing
Soil Science

Background:

Accurate soil characteristic mapping is crucial for various applications.
Satellite imagery and digital elevation models (DEM) offer potential for large-scale soil property estimation.

Purpose of the Study:

To evaluate machine learning methodologies for estimating multiple soil characteristics across Europe.
To assess the contribution of Sentinel-3 multispectral imagery and DEM derivatives to prediction accuracy.

Main Methods:

Utilized various machine learning algorithms for soil property estimation.
Integrated Sentinel-3 multispectral satellite imagery and DEM derivatives as input data.
Analyzed feature importance to determine the contribution of each data source.

Main Results:

Multispectral imagery is vital for soil property estimation.
DEM derivatives improved estimation accuracy (R2) by an average of 19%.
Soil texture estimation improved by 43% and cation exchange capacity (CEC) by 65% with DEM derivatives.

Conclusions:

Machine learning, combined with satellite and elevation data, effectively estimates soil properties.
DEM derivatives significantly enhance the prediction of specific soil characteristics like texture and CEC.
Multispectral features generally hold greater importance (60%) than DEM derivatives (40%) in these estimations.