Turn satellite data into field inspection priorities.
AgriSpectra-Q analyses real EnMAP hyperspectral scenes and returns a ranked list of spectral-anomaly zones — so field teams know exactly where to go first.
Real EnMAP data
Actual EnMAP L2A GeoTIFF scenes — 224 spectral bands, 30 m/px, UAE / Gulf region. No synthetic or simulated inputs.
Geospatial outputs
Georeferenced risk rasters, priority maps, zone boundaries (GeoJSON), and inspection-budget analysis per run.
Evidence-led action
Ranked zone cards, spectral evidence per zone, and an inspection-budget chart guide field teams to the highest-priority areas first.
HOW IT WORKS
Four steps from satellite to field
DETECT
Real EnMAP L2A hyperspectral imagery — 224 spectral bands at 30 m resolution — captures spectral signals invisible to standard RGB cameras.
PRIORITISE
Spectral-anomaly scores are computed per pixel. Scene-relative percentile thresholds convert raw signals into ranked, georeferenced priority zones.
INSPECT
Field teams receive a ranked list of spectral-priority zones — inspect the most anomalous areas first, optimising time and inspection budget.
VERIFY
Ground-truth the spectral findings on-site. Close the loop between satellite observation and field-confirmed agronomic outcomes.
Ready to run a real analysis?
Select a real EnMAP scene, execute the live engine, and receive georeferenced spectral-priority zones in under 60 seconds.
Scientific boundary: AgriSpectra-Q identifies spectral-anomaly priority candidates for field inspection. It does not diagnose disease, pests, or soil conditions. All outputs require on-site verification before any operational decision is made.