Surface saturation can have a critical impact on runoff generation and water quality. Saturation patterns are dynamic, thus their potential control on discharge and water quality is also variable in time. In this study, we assess the practicability of applying thermal infrared (TIR) imagery for mapping surface-saturation dynamics. The advantages of TIR imagery compared to other surface-saturation mapping methods are its large spatial and temporal flexibility, its non-invasive character, and the fact that it allows for a rapid and intuitive visualization of surface-saturated areas. Based on an 18-month field campaign, we review and discuss the methodological principles, the conditions in which the method works best, and the problems that may occur. These considerations enable potential users to plan efficient TIR imagery-mapping campaigns and benefit from the full potential offered by TIR imagery, which we demonstrate with several application examples. In addition, we elaborate on image post-processing and test different methods for the generation of binary saturation maps from the TIR images. We test the methods on various images with different image characteristics. Results show that the best method, in addition to a manual image classification, is a statistical approach that combines the fitting of two pixel class distributions, adaptive thresholding, and region growing.