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April 10, 2026Discover EnvironmentOpen Access

Mapping of salt-affected soil using machine learning and remote sensing in Raya Kobo Valley, Ethiopia

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Authors

SASisay Dessale AbateSWSolomon WondatirTFTigabu Fenta

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Overview

Evaluates machine learning models for predicting soil salinity in an irrigated area, indicating effective mapping techniques.

Key Points

  • The study aims to assess machine learning models' accuracy in predicting soil salinity using satellite imagery data.
  • Evaluated four machine learning regression models: Random Forest, Gradient Boosting Trees, Decision Tree, and Support Vector Machine.
  • Analyzed spectral indices derived from Landsat 8 OLI imagery.
  • Collected 33 soil samples from a total area of 939.46 hectares for validation.
  • Utilized Google Earth Engine and R software for analysis.
  • Gradient Boosting Trees and Random Forest achieved high R2 values of 0.93 and 0.902, respectively.
  • Classified the study area into slightly saline (31.2%), moderately saline (49.9%), and strongly saline (18.9%).
  • Both models demonstrated lower errors and effective spatial mapping capabilities.

Cite This Study

Abate et al. (2026) studied this question.

synapsesocial.com/papers/69d893626c1944d70ce046e0https://doi.org/10.1007/s44274-026-00639-x
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