The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 degrees C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1 degrees C compared to those where no drift correction was applied.