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