Technical note: Mapping surface-saturation dynamics with thermal infrared imagery

Abstract

Abstract. 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.

Saturated areas through the lens: 1. Spatio‐temporal variability of surface saturation documented through thermal infrared imagery

“…In addition, the extension of surface saturation in the different areas may be influenced by stream expansion into the riparian zone (cf. Glaser et al, ; Figure b,e). When passing again from wet to dry conditions, PSA and PSpA showed more persistent surface saturation in the riparian zone compared with N‐PSpA (observed from TIR observations).…”

“…Workflow of the thermal infrared (TIR) image postprocessing for the example of the TIR panorama of Area M2 of February 25, 2016. For more details, the reader is referred to Glaser et al ()…”

“…For more details on the postprocessing steps, we refer to fig. 3 in Glaser et al (), where the methodology for the TIR approach was developed. We then calculated the percentage of saturated pixels in each panorama as proxy for the extent of surface saturation in the investigated areas following the manual approach for the generation of saturation maps described in Glaser et al ().…”

“…Here, we analyse the temporal variability of different riparian surface saturated areas under a new resolution and perspective—namely, “through the lens” of a TIR camera. We employed ground‐based TIR imagery in an analogous approach to Pfister et al () and Glaser et al (; 2016; i.e., to detect temperature differences between the water at the ground surface—saturated areas—and the surrounding environment—unsaturated areas), to obtain a unique dataset of 2 years of biweekly observations of different riparian surface saturated areas within the Weierbach catchment in Luxembourg. This long‐term studied headwater catchment (0.42 km 2 ) is a reference site for rainfall‐dominated mountainous catchments (Zuecco, Penna, & Borga, ).…”

Saturated areas through the lens: 2. Spatio‐temporal variability of streamflow generation and its relationship with surface saturation

“…More challenging is the mapping of riparian surface saturation, where classic approaches such as field surveys based on the "squishy boot" method may not provide an adequate spatio-temporal resolution (Pfister, McDonnell, Hissler, & Hoffmann, 2010). In this regard, recent technological development is represented by ground-based remote sensing techniques (i.e., thermal infrared [TIR] or digital imagery) with which surface saturation can be mapped at a higher temporal (i.e., minutes to weeks) and spatial (i.e., centimetres to metres) resolution (Glaser et al, 2016;Glaser, Antonelli, Chini, Pfister, & Klaus, 2018;Pfister et al, 2010;Silasari, Parajka, Ressl, Strauss, & Blöschl, 2017).…”