Complementary Ocean Data Collection using Heterogeneous Sensor Platforms

Tore Mo-Bjørkelund1, Petter Norgren1, Trygve Olav Fossum2 and Martin Ludvisen2,3, (1)Norwegian University of Science and Technology, Department of Marine Technology, Trondheim, Norway, (2)Norwegian University of Science and Technology (NTNU), Department of Marine Technology, Centre of Autonomous Marine Operations and Systemt (AMOS), Trondheim, Norway, (3)University Centre in Svalbard, Department of Arctic Technology, Longyearbyen, Norway
Mapping and measuring ocean processes can be challenging using a single sensor platform due to large temporal and spatial variability. This can be addressed by deploying heterogeneous sensor networks, where sensor platforms share information, providing context and value for future sampling, as well as reducing the temporal dependency inherited by single sensor data collection practice. We deployed a heterogeneous network containing an Autonomous Surface Vehicle (ASV) equipped with an Acoustic Zooplankton and Fish Profiler (AZFP) and an Autonomous Underwater Vehicle (AUV) equipped with CTD and a optical scattering sensor sensitive to Chlorophyll a (\textit{Chl a}) wavelength signature. The heterogeneous network of vehicles was configured to monitor descriptive transects, with the ASV acting as the main communication node. We present contemporaneous and co-located acoustic profiles from the AZFP mounted on the ASV along with \textit{Chl a} measurements from the AUV recorded in May 2019, outside Sula, Norway. The correlation of daytime zooplankton maxima in the upper water column and \textit{Chl a} maxima is investigated. We found that the daytime \textit{Chl a} maximum and and the zooplankton maximum was separated by . The large separation between the maxima shows that the experiment should be run for a full diurnal cycle. CTD data shows a temperature minimum of at depth and an upper layer of lower salinity, tidally mixed and having a positive salinity gradient towards the open ocean. The setup only allowed \textit{Chl a} as a proxy for phytoplankton biomass, however, this assumption is not always true and a more advanced sensor package should be considered in future work. The network proved the capability to collect data on a wider spatio-temporal scale. This capability can be further improved by introducing adaptive and collaborative sampling strategies.