The calibration layer for comparable Earth observation data.
RefCal is SpectraWorks' calibration transfer technology. It makes spectral measurements from different sensors, platforms, and time periods comparable, traceable, and defensible.
Clean outputs can still hide messy measurements.
Earth observation data often arrives looking polished: maps, dashboards, indices, model outputs, change-detection layers.
But underneath, the measurements may come from different sensors, calibration histories, atmospheric corrections, processing chains, and assumptions.
Can this measurement really be compared to that one?
RefCal is built for that question.
A reference point for spectral data.
RefCal is not another analytics dashboard. It is not a visual interpretation layer. It is a calibration transfer technology: a way to anchor spectral measurements from heterogeneous sensors so data from different sources can be compared with greater confidence.
It is designed for teams who need to understand not only what the data appears to show, but how reliable that measurement is.
Post-processing cannot fix every measurement problem.
Many Earth observation workflows rely on corrections, harmonisation, or model-based adjustments after data has already been collected. Those methods can be useful, but they often leave users with uncertainty that is difficult to see, compare, or explain.
RefCal starts earlier in the measurement chain. Its purpose is to create a stronger calibration basis before decisions are built on top of the data. When the reference point is weak, every downstream claim carries that weakness with it.
For teams who need Earth observation data to hold up.
Defensible baselines
When change detection, vegetation baselines, or emissions claims need to be defensible to auditors, regulators, and reviewers.
Claims that survive scrutiny
When claims about land use, sourcing, or environmental impact need evidence that holds up under audit, across time, regions, and providers.
Confident prioritisation
When spectral signals are used to prioritise field work, investment, or drilling decisions across vast tenements with limited ground truth.
Cross-sensor reliability
When cross-sensor reliability expands the addressable market for an instrument or constellation, by making its data interoperable with the wider EO ecosystem.
Even advanced satellite systems need reference points.
The calibration challenge is not limited to low-quality data. Even major Earth observation systems need careful cross-calibration to make measurements comparable.
When ESA brought a new Sentinel-2 satellite into operation, it flew the new unit in close formation with an existing one to cross-calibrate the two instruments. Even sensors built to the same specification need careful calibration before their data can be compared with confidence.
If decisions depend on comparison, the reference layer matters.
RefCal is built from the same principle.
Working on a problem where calibration matters?
If your work depends on comparable Earth observation data, cross-sensor reliability, or defensible measurement, start a technical conversation with us.