Traditional tools and methodologies for mesoscale observation of oceanographic phenomena are limited by under-sampling and data latency. In this article we evaluate three different scenario variants of an architecture for how heterogeneous sensor nodes can be integrated with satellite remote sensing. Independent space and marine sensing platforms are interconnected either directly or by means of a ground-based mission control center responsible for data processing, relay, and coordination of the assets. A wave-propelled unmanned surface vehicle (USV) persistently collects in situ data of the targeted phenomenon. In two variants of the architecture, a dedicated small satellite acts as a sensor node, a data processing facility and a communication node. We have used a System-of-Systems (SoS) modeling approach coupled with operational simulations in different locations on Earth, in order to support the proposed methodology and investigate quantitatively the reduction the data latency to end-users. Through a combination of field experiments and simulations we estimate how the different scenarios perform with respect to providing remote sensing data that are used to create a measurement and navigation plan for the autonomous vessel.

Over the last few decades, acoustic fish telemetry has developed into a viable approach for remote monitoring of fish behaviour in the marine environment. Simultaneously, unmanned surface and underwater vehicles have found extensive use as scalable, persistent and cost-effective platforms for ocean observation. Equipping such robotic vehicles with fish telemetry receivers constitutes an emerging approach with the potential to significantly push the current operational limits of fish movement studies at sea. Here, we present an energy-autonomous robotic fish telemetry platform realised through the integration of a real-time acoustic receiver into an ocean-going wave- and solar-powered unmanned surface vehicle. The vehicle frame and energy harvesting solutions are based on the commercially available AutoNaut USV, while the vehicle’s control and communication systems were developed using open-source software and standard hardware components. The open architecture permitted deep integration of the acoustic receiver as a system-level payload making fish detections and other sensor data available in real-time to the vehicle’s onboard control system. The vehicle is thus prepared with local situational awareness to support autonomous control during vehicle-fish encounters, as well as conventional interfaces for remote piloting and data management through long-range wireless communication links and the Internet. The vehicle concept was investigated theoretically and experimentally in an acoustic range test and a full-scale sea trial. When driven passively by waves, tag detection performance was comparable to that of traditional moored receivers, while activation of the auxiliary electric thruster caused a reduction in detection radius of more than 50%, confirming wave-power as the ideal mode of propulsion. Finally, by deploying the AutoNaut over a period of several days at the outskirts of a Norwegian fjord during the seaward migration of Atlantic salmon post-smolts, we demonstrated that the vehicle was able to detect an acoustically tagged post-smolt into the open ocean beyond the reach of the fjord’s stationary receiver grid, while using only wave- and solar energy harvesting to power its operation. The ability to observe small individual fish in the ocean environment using an energy-autonomous robotic vehicle creates novel and unprecedented opportunities for scientific inquiry in fish behaviour and movement ecology studies at sea.

The spring phytoplankton bloom plays a major role in pelagic ecosystems; however, its dynamics are not well understood due to insufficient, highly resolved observational data. Here we investigate the start, peak, and decline of a 2‐week phytoplankton spring bloom in Frohavet located in a biological hotspot of the coast of mid‐Norway. We used observations from an uncrewed surface vehicle (USV) combined with buoy measurements, satellite images, discrete water sampling and modeling approaches. The spring bloom (March–June 2022) consisted of multiple peaks (up to 5 mg Chla m⁻³), with a long peak in April dominated by the diatom Skeletonema, coincident with the period when the USV captured the temporal and spatial dynamics of the bloom. Short‐term episode of calm weather in spring, such as clear skies and consistent low wind speed (<7 m s⁻¹) shoaled the mixed layer depth (10 m s⁻¹), suggesting strong influence of the environment on phytoplankton dynamics. Understanding the dynamics of the spring bloom is crucial for predicting its impact on marine trophic web and biogeochemical cycles. Integration of distinct observational platforms has the potential to unveil the environmental factors underlying phytoplankton bloom dynamics.