Goal: Make sure the changes you detect are changes on the ground.
We characterize each imager in the lab for about two years before launch, so we know how it behaves from its first image.
We characterize each detector with an integrating sphere, collimator, TVAC chamber, and laser.
We convert raw pixel counts into radiance and reflectance, so each pixel value has the same physical meaning.
This radiometric consistency lets you compare values across time.
A brief, one-time maneuver in orbit aligns detector response across the swath, so differences in the imagery come from the ground.
Result: more uniform imagery and more consistent time series for your analytics.
We align the bands and calibrate them against each other, so multi-band analytics are reliable and repeatable.
Aligned bands make NDVI, NDWI, and cloud masks reliable.
We tie the constellation to one common calibration framework, so imagery from different sensors and satellites works as one observing system.
Result: you compare imagery directly across dates, satellites, and products.
In orbit, we validate calibration against independent ground references and other missions, then monitor and refine it so image quality stays stable over time.
Result: imagery stays comparable and analysis-ready over time.
Calibrated
Uncalibrated
Calibration puts each image on the same absolute reflectance scale, so a field gives the same reading from one day to the next and from one satellite to another.
When the signal is stable, a change in reflectance means a change on the ground. You see crop stress sooner, and forecasts built on the data stay consistent.
Radiometric accuracy and stability are independently verifiable against reference sites and across the constellation.
In-flight geometric calibration corrects pointing errors, optical distortions, and timing offsets, so each pixel lands in its correct position.
Accurate alignment keeps field boundaries in place across dates and satellites, so you look at the same area each time.
When pixels stay in place, changes in vegetation reflect real crop dynamics.
Result: more accurate yield forecasts and earlier stress detection.
Your technical team evaluates geometric quality and stability across the time series independently.
Overlay reference data to verify accuracy to a fraction of a pixel.
Registered · 3 dates aligned Drifting · sub-field error Misregistered · field contaminated
Registration controls
What it costs you
When pixels land in the right place, the same plants are measured every time – so a change in the signal means a change on the ground.
With the field drifting under its outline, your NDVI trend is measuring different ground each date – noise dressed up as change.
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