Autonomous Sensing Technologies in Marine Ecosystems

Autonomous sensing technologies have transformed our ability to observe and understand marine environments at scales previously unattainable by traditional ship-based methods. Platforms such as autonomous underwater vehicles (AUVs), gliders and fixed moorings now routinely carry suites of physical, chemical, biological and optical sensors, enabling high-resolution mapping of temperature, salinity, oxygen, pH, chlorophyll fluorescence and other key variables. Advances in miniaturisation, power management and data transmission have extended mission durations from days to months, fostering sustained monitoring of dynamic processes such as ocean fronts, eddies and harmful algal blooms. Recent work has integrated novel molecular sensors and genomic samplers to capture microbial community composition in situ, while machine-learning algorithms applied to hyperspectral remote-sensing data have enhanced detection of phytoplankton functional types. The evolution of interoperable sensor networks, combined with real-time telemetry and cloud-based data processing, is enabling adaptive sampling strategies that respond to evolving conditions. These developments carry global significance for climate monitoring, marine resource management, pollution tracking and conservation of vulnerable habitats.

Research from Nature Portfolio

Recent studies have demonstrated the power of coordinated, long-duration expeditions that deploy multiple autonomous platforms across diverse habitats. A comprehensive investigation of coral reef and surface-ocean ecosystems used integrated sensor arrays on AUVs and sampling vessels to characterise biogeochemical gradients and microbial diversity over thousands of kilometres. This work highlighted how sustained autonomous measurements of temperature, nutrients and fluorescence, paired with high-throughput molecular sampling, reveal fine-scale drivers of reef health and resilience. Analyses of these datasets have also underscored the value of open-access repositories for fostering cross-disciplinary insight into ecosystem connectivity and response to climatic stressors.

Autonomous Sensing Technologies in Marine Ecosystems publication trend

The graph below shows the total number of articles in autonomous sensing technologies in marine ecosystems across all publications each year (not limited to Nature Index journals).

Technical terms

Autonomous Underwater Vehicle (AUV): A self-propelled, untethered submersible platform equipped with sensors for in situ marine measurements.

Glider: A buoyancy-driven autonomous vehicle that cycles between the surface and depth to collect long-duration oceanographic data.

Hyperspectral Remote Sensing: The acquisition of fine-scale spectral data across many narrow wavelength bands to characterise water properties and phytoplankton composition.

Variational Autoencoder (VAE): A generative machine-learning model used to infer latent representations for complex multidimensional data, such as spectral reflectance.

Environmental Sample Processor (ESP): An underwater, autonomous instrument that collects, processes and analyses fluid samples for molecular markers of microbial or harmful-algal species.

References

1. Insights and achievements from the Tara Pacific expedition. Nature Communications (2023).
2. Variational Autoencoder Framework for Hyperspectral Retrievals (Hyper-VAE) of Phytoplankton Absorption and Chlorophyll a in Coastal Waters for NASA’s EMIT and PACE Missions. IEEE Transactions on Geoscience and Remote Sensing (2025).
3. Open science resources from the Tara Pacific expedition across coral reef and surface ocean ecosystems. Scientific Data (2023).
4. Sensors for Coastal and Ocean Monitoring. Annual Review of Analytical Chemistry (2023).
5. Advancing Observation of Ocean Biogeochemistry, Biology, and Ecosystems With Cost-Effective in situ Sensing Technologies. Frontiers in Marine Science (2019).
6. Targeted Sampling by Autonomous Underwater Vehicles. Frontiers in Marine Science (2019).
7. Development of an autonomous biosampler to capture in situ aquatic microbiomes. PLOS ONE (2019).