EGU24-12818, updated on 09 Mar 2024
https://doi.org/10.5194/egusphere-egu24-12818
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Measuring river surface velocity using UAS-borne Doppler radar

Zhen Zhou1, Laura Riis-Klinkvort1, Emilie Ahrnkiel Jørgensen1, Monica Coppo Frías1, Peter Bauer-Gottwein1, Alexander Rietz Vesterhauge2, Daniel Haugård Olesen2, Alexey Dobrovolskiy3, Alexey Kadek3, Niksa Orlic4, Tomislav Grubesa4, Henrik Grosen5, Sune Nielsen5, Daniel Wennerberg6, Viktor Fagerström6, Jenny Axén6, and David Gustafsson6
Zhen Zhou et al.
  • 1DTU Sustain, Technical University of Denmark, Kgs. Lyngby, Denmark (zhezh@dtu.dk)
  • 2DTU Space, Technical University of Denmark, Kgs. Lyngby, Denmark
  • 3SPH Engineering, Riga, Latvia
  • 4Geolux DOO, Samobor, Croatia
  • 5Drone Systems Aps, Aarhus, Denmark
  • 6SMHI Sveriges Meteorologiska och Hydrologiska Institut, Norrköping, Sweden

Compared to traditional river surface velocity measurement techniques such as in-situ point measurements with electromagnetic current meters, remote sensing techniques are attractive because measurements are fast, low cost and contactless. Based on Unmanned Aerial Systems (UAS) equipped with optical equipment (e.g., HD camera) and Doppler radar, surface velocity can be efficiently measured with high spatial resolution. UAS-borne Doppler radar is particularly attractive, because it is suitable for real-time velocity determination and has fewer limitations (no seeding of the flow required, no daylight required, works for both narrow and wide rivers).

In this paper, videos from a UAS RGB video camera were analysed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a 24 GHz continuous wave Doppler radar (e.g., Geolux RSS-2-300) at multiple waypoints across the river. Different from previous processing methods, which only considered the processed velocity from Doppler radar itself, we propose an algorithm for picking the correct river surface velocity from the raw data. The algorithm fits two alternative models to the raw data average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peaks model.

Results indicate that river flow velocity and drone-induced propwash velocity can be found in the river’s lower flow velocity portions (i.e., surface velocity between 30 cm/s and 80 cm/s), while the drone-induced velocity can be neglected in fast and highly turbulent flows (i.e., surface velocity > 80 cm/s). To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of electromagnetic velocity sensor data (OTT MF Pro) with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river flow velocity.

How to cite: Zhou, Z., Riis-Klinkvort, L., Ahrnkiel Jørgensen, E., Coppo Frías, M., Bauer-Gottwein, P., Rietz Vesterhauge, A., Haugård Olesen, D., Dobrovolskiy, A., Kadek, A., Orlic, N., Grubesa, T., Grosen, H., Nielsen, S., Wennerberg, D., Fagerström, V., Axén, J., and Gustafsson, D.: Measuring river surface velocity using UAS-borne Doppler radar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12818, https://doi.org/10.5194/egusphere-egu24-12818, 2024.