Combining a novel in-situ sensor system with remotely sensed thermal infrared data to analyse spatial water temperature patterns in 3D

Abstract

To monitor aquatic habitats and understand physical and biogeochemical processes, the analysis of high-resolution spatial patterns in water temperature is of outmost importance. The spatial resolution of remotely sensed thermal infrared (TIR) data ranges from cm-scale for airborne to m- and km-scale for spaceborne observations, enabling the analysis of a variety of hydrological processes. However, while remotely sensed TIR data reflects temperatures emitted from the direct surface, the temperature of waterbodies may also vary significantly with depth. Hence, there are limits to relying purely on remotely sensed water temperature data to understand 3D water temperature patterns, leading to the need of high-resolution 3D water temperature data.Here, we combined a novel self-build in-situ sensor system with remotely sensed TIR data to explore high-resolution, natural and anthropogenically influenced 3D spatio-temporal patterns in river water temperature. The study site involved a ~780 m long stretch of a river in the Northeast of Scotland, with a smaller section (~50m long) that is influenced by cooling water being discharged from a local distillery. We applied our new observation system to gain a better understanding on the 3D extend of a thermal plume and how this local anomaly compares to and affects the overall thermal variability within the river. Three surveys were conducted (during April-June 2021) to measure the surface water temperature of the river with an UAV based TIR camera. We additionally installed the novel in-situ sensor system to measure 3D water temperature during each survey. The surveys were planned to acquire data under contrasting ambient conditions as well as at a time when no cooling water was being discharged, allowing us to also observe spatio-temporal thermal variability under natural conditions. While the acquired TIR datasets give an overall view of the thermal variability at the surface, subsets of the TIR datasets were merged with the corresponding data from the in-situ sensor system to spatially interpolate high resolution 3D water temperature of the area influenced by the thermal plume. The results show that (I) the combination of remote sensing and sensor system can detect pattern in 3D in high spatial resolution, (II) surface temperatures and their spatial patterns differ from temperatures and their spatial patterns at greater depth and (III) at this site, local anomalies due to cooling water releases do not alter the overall thermal variability within the river.The combination of the novel sensor system with remotely sensed TIR data has the potential to be used to observe a broad range of hydrological processes in natural and artificial aquatic environments and to contribute to the understanding of overall energy budgets, infiltration, limnology, groundwater surface water exchange or similar processes.