Mitigating Radiometric Bias in Unmanned Aerial Vehicle Thermal Infrared Imagery Using Temperature-Controlled Reference Plates

Abstract

Temperature retrieval from unmanned aerial vehicle (UAV) thermal imagery commonly relies on proprietary manufacturer radiometric calibration software. However, the correction procedures used by these workflows are generally undisclosed, and some user-defined parameters, particularly altitude, are restricted to ranges that do not reflect real-world conditions, risking the introduction of bias and higher radiometric uncertainty. Accordingly, this study evaluates the accuracy of proprietary radiometric calibration by examining how its performance changes with flight altitude, while also proposing a practical method to correct residual bias after proprietary processing. Thermal imagery is collected with a DJI Matrice 300 RTK equipped with a Zenmuse H20T sensor at 15, 30, 45, and 60 m. Two temperature-controlled plates (TCPs) spanning cold and hot regimes are used to validate three DJI Thermal SDK workflows: (1) default settings; (2) custom settings using measured emissivity, flight altitude, humidity, and reflected apparent temperature (Treflected); and (3) custom settings with per-image tuning of Treflected. Residual bias is subsequently corrected using empirical line calibration (ELC) derived from TCP observations. Across all proprietary workflows, temperature error increases with altitude, indicating progressive gain and offset bias not fully corrected by manufacturer processing. Default settings produce the largest error, with mean absolute error increasing from 4.39°C at 15 m to 11.69°C at 60 m. Custom settings reduce error moderately (3.86°C to 9.43°C), while Treflected tuning improves close-range performance but does not eliminate altitude-dependent degradation (0.99°C to 8.57°C). Application of ELC substantially reduces residual bias across workflows, with best performance achieved by combining Treflected tuning with ELC, resulting in sub-1°C accuracy across altitudes. These results demonstrate that proprietary UAV thermal image processing alone is insufficient to ensure robust temperature retrieval but that a simple empirically calibrated postprocessing workflow using TCPs can mitigate altitude-dependent bias and enable high-accuracy temperature estimation in operational remote sensing applications