Overview
HYDRA-EO Tutorials · Hybrid ML & RTM for crop stress
This tutorial site provides lightweight examples, scripts and notebooks that illustrate the main concepts of the HYDRA-EO project: hybrid machine learning, radiative transfer modelling and multi-sensor Earth Observation for crop stress, pests and disease monitoring.
What are these tutorials?
The HYDRA-EO tutorial platform mirrors the structure of the main HYDRA-EO project website, but in a lightweight scientific format. It provides a sandbox where users can interact with radiative transfer models, explore spectral simulations, test machine-learning workflows and understand how symptoms of crop stress emerge across spectral domains.
Radiative transfer simulations
Hands-on examples using ToolsRTM (PROSAIL, INFORM, FLUSPECT, SPART, MARMIT) and SCOPEinR for coupled radiative transfer, energy balance and fluorescence.
Trait-based machine learning
Practical workflows showing how synthetic RTM libraries can support supervised learning, stress differentiation and band/feature selection for ESA missions.
Multi-sensor Earth Observation
Use cases combining UAV hyperspectral, thermal, airborne HyPlant, and satellite data (Sentinel-2, PRISMA, EnMAP, FLEX-like SIF) to explore scaling from leaf to landscape.