theRmalUAV: an R package to clean and correct thermal UAV data for accurate land surface temperatures

Thermal cameras mounted on unoccupied aerial vehicles (UAVs) are increasingly utilized across various environmental research fields, including hydrological modelling, wildfire detection, urban heat island studies, microclimate and precision agriculture. However, several steps are needed to convert the measured thermal signal to more relevant land surface temperature (LST). Since a number of users may have limited expertise in thermal remote sensing or data processing, necessary thermal corrections are often neglected or not performed correctly in research, even though this can result in substantial discrepancies of up to 5 °C in extreme cases when absolute LST is required. We facilitate the processing by introducing a new R package, theRmalUAV, which offers two workflows: an orthomosaic-based and an image-based workflow. The orthomosaic workflow consists of a single function to apply on an orthomosaic, while the image-based workflow provides greater flexibility, accommodating intra-flight variations in atmospheric conditions. Key components of the package include correcting for atmospheric interactions, background temperature, spatial emissivity using NDVI and land cover, and the influence of changing weather conditions on LST. Additionally, we introduce a novel method for accounting for rapid changes in illumination during flights. The package also includes functions for data cleaning, co-registration, and reporting. The package currently supports 11 different thermal sensors, covering the vast majority of thermographic cameras used today. The importance of these corrections and the implementation of the package are demonstrated through two use cases involving TeAx and DJI thermal cameras, under both ideal and challenging conditions.