ML Mapping of Soil Salinity for Land Degradation

Maps salinity risk and trends using ML with terrain, climate, land use, and remote sensing to guide mitigation.

This project maps soil salinization as a land degradation process using machine learning and multi-source environmental data. We link measured salinity indicators to drivers and symptoms captured by terrain/hydrology, climate, land use/irrigation proxies, geology, and remote sensing signals. Models are evaluated with spatially robust validation to ensure realistic generalization. Outputs include salinity maps and, when feasible, risk/confidence layers to guide monitoring and mitigation.

Main focus

  • Data-driven salinity mapping to support land degradation mitigation.
  • Integrate drivers (climate/terrain/land use) with remote sensing evidence.
  • Communicate risk and reliability to prioritize actions and sampling.

Objectives

  1. Compile salinity observations and relevant driver covariates for modelling.
  2. Train ML/ensemble models with spatial validation for realistic performance.
  3. Deliver salinity + risk/confidence layers for targeted monitoring and intervention.

Graphical abstract

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References