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UC 86 — WaterStress

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Key Information
Title
Integration in the Agrifood Sector Federated Data Space of Andalusia: Upscaling the Crop prediction Use Case to the European Space
Acronym
WaterStress
Coordinator
University of Malaga (Spain)University
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Earth Observation & Remote Sensing Products
Sectors
Arable cropFruitsVegetables
Data Types
Machine sensor dataEarth observation dataFarm Management Information Systems (FMIS) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.1 Data acquisition & re-use framework
  • 3.1.2.2 Link databases & computing capacities
  • 3.1.2.4 Reference data sets & non-discriminatory data
  • 3.1.2.5 Standardized metadata scheme & ontologies
  • 3.1.2.7 Multi-layer geospatial data tool with API
  • 3.1.2.12 Solutions for private/public interests
  • 3.1.2.13 Procedures to aggregate sensitive data

3.1.4 Applications of AI techniques

  • 3.1.4.1 Identify key reference/training data sets
  • 3.1.4.2 Capitalize historical satellite data
  • Privacy law solutions for satellite imagery
Themes
Crop production & monitoringData infrastructure & governanceAI / ML / Decision Support
Partners (2)
  • University of MalagaP1
    Coordinator

    SpainUniversity

  • Regional Ministry of Agriculture. AndalusiaP2

    SpainOther

#OrganisationCountryType
P1University of MalagaCoordinator

Introduction WaterStress is oriented to provide users with a tool to monitor water use in croplands at farm and/or agriculture organisation level (cooperatives, farm associations, etc.) for any location in Europe. In this service, satellite data from the Copernicus program is processed and integrated to provide historical records of leaf water content to detect anomalies (drought or over-irrigation) in crop moisture at a concrete time. WaterStress is an extension of a Use Case developed within the Andalusian Technological Demonstrator for the Agri-Food sector, generated for the project “Towards the creation of the Federated Data Space of the Andalusian Agri-Food Sector (EDAAn)”. Methodology The users must provide the coordinates of the polygon that delimits a particular crop parcel or area, in one of the following formats: zipped shapefile, geojson, CSV or cadastral code. The users must also set the time frame of which they would like to analyse the water content of their crops. The full Sentinel-2 time series (2015-2026) is processed at a monthly rate, to calculate NDWI, a spectral index that highlight water content in plants, to analyse the historical behaviour of water content of the crops within the selected area. Then, a plot shows the historical water content of the cropland from 2015 to 2026, highlighting the selected period. A second plot aggregates the decadal trend monthly, and compares the selected period against the normal situation, allowing the detection of over-irrigation (positive anomaly) or drought (negative anomaly). Output WaterStress delivers several outputs: (1) the images of monthly NDWI of the cropland, over the selected time frame, (2) the plots of monthly NDWI along the last decade and the plot with the monthly trend and anomalies, and (3) a report in PDF with all outputs. Impact WaterStress allow farmers to detect mismanagement of water resources and optimise irrigation, depending on their crop type water requirements. What I can offer An app to optimise irrigation and water management resources in croplands What I need Only the coordinates of your cropland

Source: Annual AgData Use Case Summit 2026 booklet.

Test and experimental facilities (TEF research) data
Models & Macro data
Statistics Registers data
Public administration data
3.1.4.3
  • 3.1.4.4 AI handling heterogeneous & fuzzy information

    • 3.2.1 Enhancing functionality of and generating input for DSS including FMIS

    • 3.2.1.1 Data layers & algorithms for FMIS services
    • 3.2.1.2 Extrapolate farm-generated sensor data
    • 3.2.1.3 New satellite imagery & ground sensors for DSS
    • 3.2.1.8 Business models demonstrating ROI

    3.2.3 Assessment of farm performance

    • 3.2.3.1 Thematic areas for farm metrics
    • 3.2.3.2 Ambitious farm performance targets

    3.2.4 Data-based solutions for addressing environmental challenges

    • 3.2.4.2 Prescription maps for precision cropping
    • 3.2.4.5 Continuous soil & water sensor monitoring

    4.3 Uptake & innovation management (R&I activities)

    • 4.3.1.2 Communicate value to end-users
    • 4.3.1.3 User-friendly data platforms
    • 4.3.1.6 Training in advanced digital skills (DEP)
    • 4.3.1.11 Promote open science
    • 4.3.1.12 Communication, brokerage & events
    Spain
    University
    P2Regional Ministry of Agriculture. AndalusiaSpainOther