The ocean is complex, we need more (and better) data

The ocean is highly dynamic and deeply interconnected. Physical, chemical, and biological processes influence each other across long timescales. To understand, and ultimately impact, such a vast and constantly changing environment, we need an equally vast and consistent supply of data.

Unfortunately that data doesn’t exist yet. To characterize ocean complexity, we need simultaneous, in-situ measurements that can be correlated and interpreted together. And collecting those measurements at scale is where most programs run into trouble.

Every time you add “just one more sensor,” you also add power requirements, integration work, maintenance planning, and downstream data wrangling. Too often, the result is a patchwork of instruments and disconnected datasets that are difficult to scale and even harder to operate reliably.

As the Head of Spotter Product, I often work with incredible teams who spend far too much time preparing and stitching together their data and not enough time being able to act on it.

The problem with single-sensor programs

Single-sensor deployments are useful for baseline environmental measurements or regulatory compliance. But the moment your question becomes “why is this happening?” or “what will happen next?” a single measurement falls short.

Take for example harmful algal bloom (HAB) forecasting. Chlorophyll-a may help detect early formations, but without environmental drivers like photosynthetically active radiation (PAR), dissolved oxygen (DO), and temperature, it’s difficult to determine why or how the bloom is accelerating. Without surface waves, wind, and subsurface currents, you can’t predict where the bloom will move.

More sensors unlock richer insight, but also create more operational burden. And deeper insight shouldn’t require a fresh engineering effort every time you scale a program.

Barriers to scaling multi-sensor ocean observation

Across use cases, most teams run into the same constraints. They know what they need to measure but making it affordable, scalable, and operationally realistic is the hard part. That’s a big reason the ocean data gap persists.

1. Hardware integration friction

A single marine-grade sensor isn’t cheap, and a few of them can easily exceed a budget. Add the specialized engineering required to make power, connectors, communications protocols, and environmental tolerances work together and it becomes prohibitively expensive.

2. Telemetry and retrieval tradeoffs

Choosing between “a little data now” and “a lot of data later” is one of the hardest tradeoffs in oceanography. Satellite transmission is pricey, so teams often throttle sampling just to stay within budget. Self-logging instruments avoid those telemetry costs, but they delay analysis and require vessel time for recovery and redeployment.

3. Downstream data workload

Even after the data is collected, someone still has to spend an excruciating amount of time extracting, aligning, QC’ing, interpreting, and merging it. And because every deployment uses a different sensor configuration, this becomes bespoke work that doesn’t scale.

What Sensor Fusion Is (and what it Is Not)

Sensor fusion offers a path out of this complexity. It is the process of combining measurements from multiple in-situ sensors at the edge to produce a more complete, accurate, and meaningful view of the ocean environment. Instead of treating each instrument as an isolated stream, sensor fusion integrates and interprets data collectively on-site.

Just as important:

Sensor fusion is not…

• …just bolting more sensors onto a platform.
  Without alignment, metadata, and quality control, more sensors just create more noise.
• …pushing all the complexity downstream.
  If the only way to use multi-sensor data is manual post-processing, you don’t have a sensing solution, you have a workflow problem.
  

Done well, sensor fusion minimizes one-off integrations, reduces telemetry loads, and simplifies everything downstream. The goal isn’t “more data” — it’s better, faster, decision-ready outputs.

How Sensor Fusion improves data quality, decision speed, and scalability

Sensor fusion delivers value in three compounding ways:

1. Improved data quality

• Enhanced measurements: Supporting observations dynamically improve accuracy — for example, using salinity and temperature to correct dissolved oxygen concentration.
• Context-aware QC: Measurements are interpreted on the device, allowing it to detect drift, fouling, or outliers, and drop anomalous data before it ever leaves the platform.

2. Faster decision-making

Once enriched and quality-controlled, fused measurements enable true edge intelligence.

• Threshold-based decisioning: On-device logic can determine what actions to take, such as increasing sample rates, triggering alerts, or initiating event-driven sampling, without waiting for a telemetry round trip. 

3. Non-constrained scaling

More accurate measurements and faster local decisioning make it possible to scale programs efficiently by investing in:

• Adding new sensors: Automated fusion and standardized interfaces eliminate custom integration work, making new measurements easy to adopt.
• Increasing spatial coverage: Deploy heterogeneous fleets of distributed systems to cover more area without multiplying operational burden.
• Improving applications: With less time spent on data wrangling, teams can focus on developing insights and higher-value, use case-specific tools.

Why a Platform Approach Works

This is where the Spotter Platform comes in. Spotter is a modular, rapidly deployable marine sensing platform that delivers real-time surface and subsurface data. It already provides core metocean information that many programs rely on, and it’s designed to support an expanding ecosystem of payloads rather than a single fixed configuration. This platform approach matters because it lets you add sensors and modify use cases without rebuilding the system's architecture each time.

For example, connecting a Bristlemouth-enabled Salinity Sensor to Spotter Sound allows the hydrophone to automatically compensate for real-time acoustic velocity, improving detection accuracy without any manual intervention. That kind of edge-level intelligence cuts down telemetry bandwidth and shortens the path from measurement to insight.

In practice, that means:

• Standardized connectivity: A plug-and-play hardware ecosystem with minimal integration friction — think USB, but for the ocean.
• Shared conventions: Consistent timestamps, metadata, and location information so every sensor speaks the same language.
• Efficient telemetry strategy: Sending what matters, not everything, so bandwidth and power are treated like a budget.
• Edge intelligence: The ability to host, update, and redeploy decision logic directly on the device — no new custom pipeline for every deployment.

Sensor fusion is the discipline that turns these traits into real, repeatable customer value.

How to evaluate a Sensor Fusion solution (a buyer's checklist)

If you’re evaluating using Sensor Fusion for a project, here are the questions you need to ask:

Measurement & Outputs

• What decisions do we need to support, and what underlying measurements drive those decisions?
  
• Which trusted products exist today to capture these measurements? Do we need to build our own?
  
• When would other measurements, if combined, improve our decision making ability?
  

Operations & Scale

• What are our endurance and dwell requirements? How do they change as sensors are added?
  
• What are our decision latency needs? What is our telemetry strategy to support them?
  
• What is our plan to detect and mitigate the impact of drift, fouling, or failure?
  

Integration & Future-Proofing

• How hard will it be to add a new sensor type?
  
• Are we locked into a closed ecosystem?
  
• How flexible and intelligent do our data pipelines need to be?

Closing

The ocean data gap is narrowing as monitoring shifts from isolated deployments to intelligent networks of systems. By co-locating and combining measurements at the edge, we can transform stale isolated observations into real-time actionable local insights.

This is why we at Sofar have made sensor fusion fundamental to our extensible Spotter Platform approach. It allows teams to capture what they need today and grow into the future without reinventing their workflows every time they add another sensor.