Orbispect turns raw satellite archives into decision-grade signals for agriculture and water risk: insurance triggers, water-security analytics, compliance evidence, yield outlooks. Per parcel, per district, per basin, always with quantified uncertainty.
Two decades of satellite-derived river discharge, processed in-house into a continent-wide record of how much water is actually moving through Europe's river network. Below is the measured discharge anomaly for the most recent complete August in our record — August 2025 — set against the long-term August normal. These are real observations, not a forecast.
Measured river discharge anomaly across Europe, processed in-house. Real data — not a live system, not a forecast. Contains modified third-party Earth-observation data.
See how we measure it →Our flagship, the AgroTransformer yield engine, leads four risk products built for institutions that carry agricultural exposure. The water package extends the same engine, and country monitoring packs ship as add-ons.
A deep sequence model trained on two decades of official EU yield statistics, fused with daily satellite and climate time series. Rather than guessing from pixels alone, it's anchored to a process crop model, so the network only has to learn what the physics misses.
A change-detection capability for infrastructure, land use and unauthorized construction. We hold the imagery archive and the methods to compare two passes of the same place and flag what physically changed between them — built as a capability, and not yet a live operational product.
Where war or politics cut off official statistics, the fields are still there, and satellites still see them. So we estimate planted area and crop condition in these data-void countries, calibrated against published scientific studies.
We measure the full water column: surface balance from weather, evapotranspiration and soil moisture, and groundwater from gravity, then fuse the two. One engine, both halves of the cycle — built for institutions whose balance sheets depend on water.
We measure the change in the continent's water mass itself from orbit, one of the most sensitive water signals we can read. Our engine turns that planetary signal into a district-level map of structural decline, where red marks a trend rather than just a dry month.
We measure the whole water column and fuse it: deep groundwater from orbital water-mass sensing, surface water and river flow from the balance. The mass signal alone sees regions, streamflow alone sees rivers, and together — downscaled to district level — they become one local decision tool nobody else sells.
The ground moves by millimetres a year — sinking aquifers, settling foundations, slipping slopes — long before a crack is ever visible. We hold the methods and the archive to read that motion from orbit at millimetric precision, built as a capability on layers we already run, though not yet a live operational product.
Reservoirs silently lose capacity to sediment every year. HydroSed reads sedimentation, snowpack and inflow from orbit, so generation risk gets read off the whole catchment instead of just the gauge.
Niezbędnik Rolnika is our free decision app for farmers, and the proving ground for every commercial model we ship. It opens the day's spraying windows, reads the weather and field-operations outlook, and turns each of your own parcels into a live picture: satellite crop condition, a running water balance, and alerts the moment a window opens or closes.
Real fields and full seasons of farmer decisions pressure-test every layer before it ever reaches an institutional portfolio. Same engine, same satellites, handed first to the people who actually walk the soil.
Our team is preparing the release across 8+ European countries — the same toolkit, localized per country, weather and crop calendars included.
Optical imagery is table stakes; every vendor has it. Orbispect fuses the instruments most never touch — orbital water-mass sensing, ground-motion radar, thermal from the ISS, many-band spectral chemistry — plus the mathematics that turns all of it into one answer.
We sense the change in water mass itself from orbit — the only way to see deep groundwater from space.
Millimetre-per-year ground motion from phase shifts between radar passes — subsidence and soil stability, through cloud.
High-resolution canopy temperature radiometry — crop water stress detected days before it is visible to any camera.
200+ spectral bands read soil carbon, clay and plant nitrogen as chemical signatures — lab analysis from orbit.
Methane column retrievals screen super-emitters — the climate liability most portfolios cannot see.
Microwaves that penetrate the canopy to read root-zone soil moisture — the variable rain gauges never see.
Five decades of surface temperature archive — a multi-decade climate baseline.
Ensemble Kalman assimilation, Bayesian structural time series, Gaussian-process error fields, copula risk models — the layer most vendors skip.
Operators and markets quietly restate their numbers after the fact, and only the smoothed, corrected figure survives. We snapshot every feed the moment it publishes and keep every version, so we hold the as-stated vs restated trail nobody else can reconstruct. And history only accrues from the day collection starts — it can't be bought backwards.
Every estimate ships with a confidence interval, because we'd rather say "we don't know" than fabricate a number. When a source is unavailable, the output says so.
Our models are anchored in peer-reviewed agronomy and hydrology, with citations available to clients and auditors — not proprietary magic.
Designed from day one for CSRD and insurance audit trails: versioned data lineage, reproducible runs, EU data residency.
The engine is country-agnostic — the same satellites fly over every basin on Earth. Today the deepest coverage is Central Europe, and every new market is just a configuration away, not a rebuild.
A pilot starts with your parcels, basins or assets — and ends with a side-by-side validation report your board can read. For insurers, lenders, utilities and supply-chain teams.
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