EATEM: A Method for Estimating Equivalent Acquisition Time of Pixels in UAV Thermal Infrared Mosaics

High spatial resolution land surface temperature (LST) has widespread applications in many fields. Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is a crucial means of obtaining such data. UAV thermal cameras typically need to capture numerous images to create a comprehensive TIR mosaic covering the target region, which is then converted into an LST map. However, LST can change rapidly over time, leading to temporal inconsistencies within the LST map, thereby affecting subsequent analysis and decision-making. Although reducing the UAV flight duration can minimize such inconsistencies, most practical applications cannot meet this requirement. Therefore, acquiring the time information of UAV TIR mosaic pixels is essential for developing temporal normalization methods and for assessing temperature data quality. Here, we propose a so-called equivalent acquisition time estimation for mosaics (EATEMs) method, designed to estimate the equivalent acquisition time (EAT) of UAV TIR mosaic pixels. This method integrates principles of UAV photogrammetry and image fusion. In our experiments, the estimated time map accurately reflects the UAV’s flight path and landing situation. Additionally, evaluation results based on ground-measured data indicate that the estimated time has an uncertainty of less than 5 min when there is a good linear relationship between LST and time. The more significant the linear relationship, the smaller the uncertainty. These promising results demonstrate the potential of the EATEM method in addressing issues related to temporal variations in UAV TIR remote sensing.