Most teams using thermal imaging assume they’re getting accurate temperature data—but in reality, radiometric measurements are highly sensitive to surface conditions, environment, and how the system is deployed. Small miscalculations in emissivity, reflectivity, or distance can lead to misleading readings that impact maintenance decisions and asset reliability. In this whitepaper, MultiSensor AI breaks down the key factors that influence radiometric accuracy - and how to account for them in real-world industrial environments.
Thermal cameras don’t measure temperature directly—they interpret infrared energy emitted from surfaces. That means accuracy depends on more than just the device itself. Surface characteristics, atmospheric conditions, and image resolution all play a critical role in whether your data reflects reality or introduces hidden error.
This guide provides a practical, engineering-focused overview of where measurement errors come from—and how to avoid them—so your team can rely on thermal data for predictive maintenance, inspection, and operational decision-making.
How radiometric thermal cameras convert infrared energy into temperature measurements
Why emissivity and reflectivity can significantly distort readings—and how to account for them
How atmospheric conditions (humidity, distance, air density) impact measurement accuracy
The “spot-size effect” and why resolution determines whether your readings are reliable
Best practices for improving measurement accuracy across real industrial environments
Reliability & maintenance leaders
Condition monitoring & inspection teams
Engineers working with thermal imaging or predictive maintenance
Operations teams responsible for uptime and asset performance