Following publications have been announced by our department Optical Oceanography. For further information please contact the marked authors of the publications:
Carlson, D.F., Akbulut, S., Rasmussen, J.F., Hestbech, C.S., Andersen, M.H., & Melvad, C. (2023): Compact and modular autonomous surface vehicle for water research: The Naval Operating Research Drone Assessing Climate Change (NORDACC). HardwareX, Volume 15, 2023, e00453, doi:10.1016/j.ohx.2023.e00453
Abstract:
Research, monitoring, and management of marine and aquatic ecosystems often require surface water samples to measure biogeochemical and optical parameters. Traditional sampling with a boat and several personnel onboard can be labor-intensive and safety requirements limit sampling activities in high-risk environments. This paper describes the Naval Operating Research Drone Assessing Climate Change (NORDACC). NORDACC is an open source, light-weight, and portable autonomous surface vehicle that can acquire surface water samples while also measuring sea surface temperature and salinity for the duration of its deployment. NORDACC is ideal for operations in remote areas where resources and personnel are limited. Two sample bottles, each one liter in volume, can be filled, either at pre-programmed sampling stations or manually, using the remote control. A trimaran design provides buoyancy and stability, with hulls constructed of vacuum-formed acrylonitrile butadiene styrene (ABS) plastic. NORDACC can navigate autonomously between waypoints and features first person view capabilities for enhanced situational awareness. NORDACC’s performance was validated in Aarhus Bay, Denmark, collecting multiple surface water samples in winds in excess of 8 ms−1 and steep, choppy waves.
Hieronymi, M., Bi, S., Müller, D., Schütt, E.M., Behr, D., Brockmann, C., Lebreton, C., Steinmetz, F., Stelzer, K., & Vanhellemont, Q. (2023): Ocean color atmospheric correction methods in view of usability for different optical water types. Front. Mar. Sci., 10:1129876, doi:10.3389/fmars.2023.1129876
Abstract:
Satellite remote sensing allows large-scale global observations of aquatic ecosystems and matter fluxes from the source through rivers and lakes to coasts, marginal seas into the open ocean. Fuzzy logic classification of optical water types (OWT) is increasingly used to optimally determine water properties and enable seamless transitions between water types. However, effective exploitation of this method requires a successful atmospheric correction (AC) over the entire spectral range, i.e., the upstream AC is suitable for each water type and always delivers classifiable remote-sensing reflectances. In this study, we compare five different AC methods for Sentinel-3/OLCI ocean color imagery, namely IPF, C2RCC, A4O, POLYMER, and ACOLITE-DSF (all in the 2022 current version). We evaluate their results, i.e., remote-sensing reflectance, in terms of spatial exploitability, individual flagging, spectral plausibility compared to in situ data, and OWT classifiability with four different classification schemes. Especially the results of A4O show that it is beneficial if the performance spectrum of the atmospheric correction is tailored to an OWT system and vice versa. The study gives hints on how to improve AC performance, e.g., with respect to homogeneity and flagging, but also how an OWT classification system should be designed for global deployment.