Post-Processing

Learn how to correct and standardize near-surface thermal data to improve accuracy and comparability.

Why Post-Processing Matters

Near-surface thermal infrared data often require correction for emissivity, reflected radiation, and atmospheric effects. While some workflows rely on default camera settings or empirical adjustments, a mechanistic correction approach ensures your data better reflect true surface temperatures and remain consistent across sites.

Post-Processing Schematic
Schematic showing thermal energy contributions detected by a sensor in an enclosure with a germanium window. Arrow sizes represent approximate relative thermal contributions, and dashes represent attenuation.
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Process with correcTIR

correcTIR is an open-source Python package and graphical interface that applies mechanistic corrections to near-surface thermal data. It's designed to be transparent, consistent, and easy to use—no matter your site or sensor.