Every signal Orbispect ships is computed with methods grounded in peer-reviewed science, validated against independent ground truth and bounded by quantified uncertainty. We do not ask clients to trust a black box — we show them the principles and the chain from raw signal to figure.
Full methodology and data sources are shared under NDA, in a secure data room.
Four stages, each versioned and reproducible. A regulator or reinsurer can replay any figure we have ever issued.
Raw radar and optical archives read directly at source; atmospheric and geometric corrections applied under our control.
Radar and optical time series aligned per parcel; cross-sensor checks reject artefacts a single instrument would pass.
Physics-based models assimilated with observation. Machine learning corrects residuals — it never replaces the physics.
Every figure ships with uncertainty, lineage and version. APIs and reports expose the same numbers — never two truths.
The core scientific machinery behind each product family, described at the level of capability and principle. Specific algorithms, parameterisations and data sources are shared with clients under NDA, in a secure data room.
How much water each crop is actually finding, tracked day by day through the season — demand against supply — on foundations established in agronomic science.
Drought read on a calibrated, multi-scale basis — meteorological, soil and groundwater stress combined into one signal, not naive percentiles.
A physically grounded yield engine, continuously reconciled with what the satellites observe. Outputs are calibrated ranges with honest bounds — never a single number pretending to certainty.
Soil-loss risk resolved on real terrain and real weather, district by district, refreshed each season.
An orbital water-mass signal brought from coarse scale to district level under physical constraints — long-term trend separated from seasonal noise, with uncertainty quantified per pixel.
Parcel structure — boundaries, strips, management zones — is delivered at decision grade. Every biophysical value stays at the sensor's native resolution and is never resampled upward. We sharpen geometry, not physics.
Radar sees structure and moisture through cloud; optical reads pigment and vigour. Two independent sensors must agree before any alert fires — disagreement triggers review, not output.
Machine learning corrects models; it does not replace them. A network that learns residuals stays honest when the season looks like nothing in its training data.
A number without an error bar is marketing. Every output carries calibrated uncertainty, and our calibration itself is tested against held-out seasons.
When a source is down we say so. No silent fallbacks, no synthetic filler. An honest gap is worth more than a fabricated value — especially in an audit.
Every released figure is tied to a model version, input snapshot and code revision. Clients under contract can request a replay of any historical output — and receive the same number, to the same value (deterministic pipeline). This is what "audit-ready" means in practice.