HYDRA-EO is a European Space Agency project that develops an operational framework to detect, quantify and differentiate abiotic and biotic crop stress. The project targets priority plant pathogens, including Flavescence Dorée in grapevine, Potato Virus Y in potato, and fungal diseases such as Septoria, while also assessing stress interactions under variable water and environmental conditions.
HYDRA-EO integrates Physically-based radiative transfer modelling approaches with machine learning algorithms to identify the spectral and spatial resolutions required to capture biotic stress signals effectively. By combining hyperspectral, thermal and multi-sensor Earth Observation data from field, airborne and satellite platforms, the project delivers early and reliable indicators to support agricultural resilience and evidence-based decision-making across Europe.
HYDRA-EO integrates UAV, satellite and RTM–ML processing for crop stress detection across studied European sites.
HYDRA-EO aims to develop and demonstrate a physics-informed, hybrid machine learning framework for early detection and attribution of multiple stressors in agricultural systems. By bridging leaf, canopy and landscape scales, the project supports ESA’s Digital Twin Earth vision for crops and prepares methodologies for upcoming missions such as FLEX, CHIME and LSTM.
The project addresses key Mediterranean and temperate crop systems (olive, citrus, pistachio, grapevine) and develops interpretable indicators of plant health based on hyperspectral, fluorescence and thermal data.
Multi-sensor datasets from field instruments, UAV platforms, airborne campaigns and satellite missions are combined with radiative transfer models to retrieve functional traits related to photosynthesis, structure and water status.
HYDRA-EO HYDRA-EO’s mission is to build an operational, multi-scale Earth Observation framework capable of detecting, quantifying and attributing crop stress across Europe. The project aims to bridge fundamental plant physiology with next-generation EO technologies, enabling early warning capabilities for agriculture and supporting ESA’s long-term vision for Digital Twin Earth.
By integrating radiative transfer modelling, hyperspectral and thermal sensing, and hybrid machine learning approaches, HYDRA-EO seeks to deliver interpretable, transferable stress indicators that can be deployed across different crops, environments and sensor platforms. Ultimately, the mission of HYDRA-EO is to accelerate the adoption of physics-guided ML in agricultural monitoring and contribute to more resilient, sustainable crop systems.
HYDRA-EO focuses on four strategic crops and a palette of biotic and abiotic stressors. Field campaigns and EO acquisitions are co-designed to capture contrasted responses across varieties, management and climate zones.
CIAG–IRIAF orchards with inoculated fungal and bacterial pathogens (e.g. Septoria spp., Pseudomonas savastanoi) under varying water regimes are monitored using UAV hyperspectral, thermal, and airborne-borne sensors to build a unique multi-sensor stress dataset.
Multi-stressor trials including Potato Virus Y and fungal infections in a dedicated phenotyping trial, combining leaf measurements, biochemical analysis, UAV imaging, and satellite observations. These experiments generate contrasted physiological responses across varieties under plant diseases.
Italian vineyard plots infected with Flavescence Dorée and additional fungal pathogens form the primary study area, enabling the development and validation of spectral–thermal indicators for early disease detection in perennial cropping systems.
Alfalfa experimental plots in Italy include controlled fertiliser treatments designed to capture a wide range of physiological responses. HYDRA-EO also investigates the spectral and thermal signatures associated with insect pressure in alfalfa, enabling early detection of pest-driven stress.
HYDRA-EO develops a hybrid modelling framework that couples radiative transfer simulations with machine learning to retrieve functional plant traits and disentangle overlapping stress signals (drought vs. disease) across multiple EO platforms and crops.
Retrieve traits such as chlorophyll content, anthocyanins, leaf area index (LAI), biomass, water status, canopy temperature, nitrogen- and red-edge-based indices, as well as SIF-related indicators. These traits are derived from hyperspectral, multispectral and thermal data using PROSAIL- and SCOPE-based simulations combined with machine learning.
Separate the spectral and thermal fingerprints of drought, fungal infections, bacterial and viral diseases, enabling early detection of biotic and abiotic stress. This HYDRA-EO analyses how these stressors interact, overlap and evolve over time, improving resilience assessments and supporting early warning systems in agricultural landscapes.
Quantify how plant trait signals degrade from UAV and airborne hyperspectral data to satellite missions such as Sentinel-2, PRISMA, EnMAP, FLEX, CHIME and LSTM, and design optimised bandsets and indices for operational monitoring. HYDRA-EO develops scaling strategies that link leaf, canopy and landscape processes, preparing methods for next-generation ESA missions.
Deliver open, ESA-oriented tools and Shiny interfaces that translate advanced radiative-transfer modelling, hyperspectral–thermal analysis and hybrid machine learning into practical decision-support applications for agronomists, advisors and policy stakeholders.
HYDRA-EO combines empirical datasets, radiative transfer models and advanced machine learning into a single workflow, aligned with ESA’s Digital Twin Earth vision.
HYDRA-EO activities, public presentations and scientific outputs are disseminated through ESA events, conferences and peer-reviewed papers. This section highlights key milestones and communication actions.
The HYDRA-EO project will be presented during the ESA Digital Twin Earth Components: Open Science Meeting 2026.
Date: 2–5 February 2026 (ESRIN, Frascati, Italy).
Meeting link:
ESA Digital Twin Earth – Open Science Meeting
Project presentation (PDF):
Download HYDRA-EO Presentation (PDF)
Professor Dr. Lammert Kooistra presents a talk entitled "Disease detection in potato crop fields using multi-modal sensing approaches from Uncrewed Aerial Vehicles" at the Hyperspectral Remote Sensing and AI in Agriculture Workshop.
Date: 4–5 February 2026 (Faculty of Science, Technology and Medicine at the University of Luxembourg).
Meeting link:
Hyperspectral Remote Sensing and AI in Agriculture
HYDRA-EO will generate peer-reviewed articles on radiative-transfer–ML inversion, stress indicators for Flavescence Dorée and Potato Virus Y, and EO-based disease early-warning systems.
A curated list of accepted papers, conference contributions and media highlights will be added here as the project progresses, providing references for ESA, national agencies and the scientific community.
Wageningen University & Research (WUR) is recruiting a postdoctoral researcher to work on the integration of remote sensing, agronomic, and genomic data within ESA- and EU-funded Earth Observation research projects, including HYDRA-EO.
View vacancy and apply (deadline 27 January)→HYDRA-EO is coordinated by Wageningen University and brings together expertise in EO services, modelling, plant pathology, genetics and agronomy from the Netherlands, Italy and Spain.
HYDRA-EO builds on existing open-source ecosystems developed by the GRS group at Wageningen University and extends them with ESA-funded modules for plant trait retrieval and stress detection.
This package provides a unified suite of radiative transfer models for simulating leaf, canopy and soil reflectance across multiple sensor resolutions. It integrates PROSPECT, FLUSPECT, Liberty, INFORM, fourSAIL and PROSAIL for detailed optical simulations and advance modelling as SPART and MARMIT for soil. These capabilities support flexible, physics-based workflows for EO data analysis and model–data integration.
→ View ToolsRTM on GitLabSCOPEinR implements the SCOPE model for simulating soil–canopy radiative transfer, photosynthesis, fluorescence and surface energy balance. It provides an R interface for producing consistent reflectance, SIF and photosynthesis products under varying stress, physiological and illumination conditions. This package supports detailed modelling of vegetation functioning in crop studies.
→ View SCOPEinR on GitLabA central Git repository will host the HYDRA-EO pipelines: RTM code, ML models, spectral libraries, trait–stressor metadata and Shiny applications. ESA and project partners will have access for collaboration, reproducibility and long-term reuse.
→ View HYDRA-EO on GitHubAn interactive web application will allow users to explore retrieval workflows, visualise scaling effects, and test different sensor configurations and bandsets for specific crops and pathogens.
Short, reproducible snippets to explore the HYDRA-EO workflows with ToolsRTM and SCOPEinR. You can copy, paste and adapt them in your own R scripts or RStudio projects.
library(ToolsRTM)
# Launch Shiny simulators for different RTMs
ToolsRTM::get.simulator(app = "SCOPE")
ToolsRTM::get.simulator(app = "getLUT")
ToolsRTM::get.simulator(app = "PROSAIL-BRDF")
# Type ?get.simulator to see more options
library(ToolsRTM)
#Get default PROSAIL input list
inputs <- ToolsRTM::inputsPROSAIL
# 2.2 Generate a LUT with 100 samples
set.seed(1234)
LUT <- as.data.frame(ToolsRTM::getLUT(inputs = inputs, nLUT = 100, setseed = 1234))
# Summarise min–max per parameter
lut_ranges <- data.frame(
Parameter = names(LUT),
Min = round(sapply(LUT, min, na.rm = TRUE),3),
Max = round(sapply(LUT, max, na.rm = TRUE),3)
)
# Display in an interactive datatable
DT::datatable(
lut_ranges,
caption = "Ranges of sampled input parameters in the LUT",
options = list(pageLength = 10)
)
# DGet soil profile
rsoil.dry <- ToolsRTM::dataSpec_PDB$dry_soil
rsoil.wet <- ToolsRTM::dataSpec_PDB$wet_soil
psoil <- 0.5
rsoil.default<- c(psoil*rsoil.dry+(1-psoil)*rsoil.wet)
wl_grid <- 400:2500 # wavelength grid in dataSpec_PDB
#Simulate PROSAIL (foursail) for each LUT row
sims <- lapply(seq_len(nrow(LUT)), function(i) {
out <- suppressMessages(ToolsRTM::foursail(
inputLUT = LUT[i, ],
rsoil = rsoil.default,
LeafModel = "PROSPECT-PRO",
spectrum.all = TRUE
))
# out has rdot, rsot, rddt, rsdt; combine to BRF using solar zenith (tts)
brf <- ToolsRTM::Compute_BRF(
rdot = out$rdot,
rsot = out$rsot,
tts = LUT[i, "tts"],
data.light = ToolsRTM::dataSpec_PDB
)
# Return tidy tibble for this run
tibble(run = i, wl = wl_grid, rho = as.numeric(brf))
})
library(SCOPEinR)
# Read configuration and LUT tables
setopts <- read.table("input/setoptions.csv", header = TRUE, sep = ",")
inputLUT <- read.table("input/LUT_input.csv", header = TRUE, sep = ",")
# Execute the SCOPE model
db.sim <- SCOPEinR::get.SCOPE(LUT = inputLUT,
options.SCOPE = setopts, get.outputs = "ALL", get.plots = FALSE)
HYDRA-EO is currently in preparation for the ESA Kick-off meeting. For questions regarding the project, collaboration opportunities, datasets or technical details, please contact the project coordinator at Wageningen University & Research.
Dr. Carlos Luis Camino González
Geo-Information Science and Remote Sensing (GRS)
Wageningen University – Department of Environmental Sciences (WU-DES)
Wageningen, The Netherlands
Email:
carlos.caminogonzalez@wur.nl
