Satellite derived bathymetries through high space-time resolution videos

Coastal bathymetric datasets are critical to improve our understanding of morphodynamic processes in the coastal area, as well as for decision-making on future adaptation measures. Originally designed for analyzing snapshots and time-exposure images from coastal video monitoring stations installed on buildings near the coastline, video techniques have enormously evolved over the last three decades. Recent advancements, e.g., extend their application to derive bathymetry estimations from ∼10-minutes videos captured at few frames per seconds (fps), allowing for more frequent and cost effective bathymetric estimations. New algorithms, such as cBathy (Holman et al., 2013) or uBathy (Simarro and Calvete, 2022), depart from the linear dispersion relation and different signal processing tools to infer bathymetry from the propagation of surface water waves (which is considered as a footprint of the bed shape). They have already been tested and applied not only to videos from coastal monitoring stations, but also to videos from UAVs (unmanned aerial vehicles, commonly known as drones).  In this work we explore the potential use of these techniques to 45-seconds satellite videos captured at 10 fps and with a spatial resolution of 1 pixel per meter. For this purpose, departing from uBathy, the nearshore bathymetry at Castelldefels beach has been estimated over a 6-months period using three satellite videos. The results are compared against those obtained from drones (ten 10-minutes videos over the same period) and from the fixed video monitoring station at Castelldefels beach (coo.icm.csic.es), recording 10-minutes videos three times daily. Furthermore, an echo-sounder bathymetric survey is used as Ground Truth for validation purposes. The proposed approach presents a promising alternative to existing techniques for satellite imagery which are based on the analysis of single multior hyper-spectral snapshots (color-based techniques). On the one hand, from previous experience, the expected errors are smaller than those typically obtained with color-based techniques for snapshots and, on the other hand, they eliminate well-known problems of color-based techniques when water turbidity is high. Holman, R., Bergsma, E. W. (2021). Updates to and performance of the cbathy algorithm for estimating nearshore bathymetry from remote sensing imagery. Remote Sensing, 3996. Simarro, G., Calvete, D. (2022). UBathy (v2.0): a software to obtain the bathymetry from video imagery. Remote Sensing, 14(23), 6139. Keywords: Remote sensing, Video imagery, Bathymetry inversion, UAV, Satellite.