Continuous Maritime Monitoring with SAR Ship Detection Time Series

Why Monitor Maritime Areas with SAR?

The ocean covers 71% of Earth's surface and hosts a vast, largely invisible economy: shipping, fishing, energy, and resource extraction. Most of this activity is tracked through the Automatic Identification System (AIS) — transponders that vessels are required to carry and transmit. But AIS has critical limitations:

• Dark vessels — Ships can disable or spoof their AIS transponder, becoming invisible to tracking services
• Coverage gaps — AIS satellite relay has latency; some ocean areas have sparse coverage
• Small vessels — Fishing boats under certain tonnage are not required to carry AIS
• AIS data access — Commercial AIS data is expensive and proprietary

Sentinel-1 SAR provides independent, open-access vessel detection that does not depend on AIS at all. Ships appear as bright point targets against the low-backscatter ocean background, and they cannot hide from radar by turning off their transponder.

How SAR Detects Ships

SAR ship detection relies on a physical principle: a large metal vessel floating on calm water creates an extremely strong radar return. The vessel's hull and superstructure act as corner reflectors — flat metal surfaces at right angles that bounce the radar pulse directly back toward the sensor with very high amplitude.

The contrast between ship returns and the ocean background (which is typically much weaker) makes ships detectable using threshold or statistical approaches:

1. Constant False Alarm Rate (CFAR) detection — Sets a threshold based on local background statistics, flagging pixels significantly brighter than their surroundings
2. Deep learning detection — Convolutional neural networks trained on SAR ship images can handle challenging conditions (near-shore clutter, wave interference)

The result is a list of ship detections with positions, estimated sizes, and confidence scores for each satellite overpass.

Coverage Compensation: A Critical Factor

Unlike land monitoring where the entire polygon is typically covered by each satellite pass, maritime monitoring has an important complication: satellite swath coverage.

Sentinel-1 does not image the same ocean area every overpass. The swath width is 250 km, and orbits are not perfectly repeated — so on any given date, your monitoring polygon may be:

• Fully covered — The entire area was imaged
• Partially covered — Part of the area was covered, part was not

If a polygon with 100 ships is only 40% covered, the detector will find approximately 40 ships. Without accounting for coverage, this looks like a sudden drop in ship density — which could be mistaken for a real decrease in vessel activity.

Extrapolation Correction

One approach is to extrapolate the detected count based on coverage fraction:

Estimated ships = Detected ships / Coverage fraction

If 40 ships were detected at 40% coverage, the estimated total at 100% coverage would be ~100 ships. This assumes uniform spatial distribution of ships across the polygon, which is often reasonable for large open-ocean areas but less valid near ports where ship density is concentrated.

Exclusion Approach

The alternative is to exclude low-coverage scenes from the time series. Set a minimum coverage threshold (e.g., 70% or 90%) and only include scenes where sufficient area was imaged. This gives a cleaner time series but with more gaps.

The right approach depends on your use case:

• Activity trend analysis → Extrapolation gives more data points
• Event detection (sudden traffic stop) → Exclusion avoids false alarms from coverage artifacts

What Maritime Time Series Reveals

Port Activity Patterns

By drawing a polygon over a port approach area or anchorage, you can track vessel count over months and years. Common patterns include:

• Weekly cycles — Ports with scheduled arrivals show regular peaks
• Seasonal patterns — Fishing ports peak with fishing seasons; tourism ports with travel seasons
• Long-term growth — Increasing vessel density over years tracks port development
• Disruptions — Sudden drops from labor disputes, natural disasters, sanctions, or COVID-19 impacts

Fishing Effort Monitoring

Fishing vessel density in a fishery area is a proxy for fishing effort. SAR time series can track:

• Whether fishing effort in a marine protected area is changing
• Seasonal concentration patterns following fish migrations
• Comparison between declared exclusion zones and actual vessel presence

Dark Vessel Detection

Ships visible in SAR that are absent from AIS records are dark vessels — potentially engaged in unreported fishing, smuggling, or sanction evasion. By correlating SAR ship counts with AIS vessel counts over time, you can estimate the fraction of non-reporting vessels.

In some poorly monitored fisheries, SAR reveals 2–5 times more vessels than AIS alone. This information is increasingly used by fisheries management authorities and NGOs monitoring illegal, unreported, and unregulated (IUU) fishing.

Infrastructure and Asset Monitoring

For offshore energy operations:

• Monitoring work vessel presence around oil platforms or wind farms
• Detecting unauthorized vessel activity in exclusion zones
• Tracking tanker loading/unloading activity at offshore terminals

Setting Up a Maritime Monitor

1. Navigate to your target sea area — Port, fishing ground, shipping lane, or exclusive zone
2. Draw your monitoring polygon over the area of interest
   • For ports: draw over the anchorage and approach area
   • For fisheries: draw the fishing ground or protected area boundary
   • For shipping lanes: draw across the lane corridor
3. Select Ship Detection from the Analysis options
4. Set start date — A longer window (12+ months) gives better seasonal baseline

Understanding the Output

The time series graph shows vessel count on the y-axis and date on the x-axis. Each data point represents one Sentinel-1 overpass. The coverage fraction for each point is recorded — use the coverage controls to switch between raw counts, extrapolated counts, and filtered (coverage-threshold) views.

Limitations and Caveats

Spatial resolution: Sentinel-1 IW (Interferometric Wide) mode has a nominal resolution of 5×20 m. Very small vessels (under ~30 m) may not produce reliable detections.

False detections: Offshore infrastructure (oil platforms, aquaculture buoys, wind turbines) creates bright SAR returns similar to ships. These appear as permanent fixtures in the time series — consistently present at fixed locations — whereas ship traffic moves between passes.

Sea state effects: Very rough sea conditions create high ocean backscatter that can mask small ships or increase false alarm rates. High-sea-state scenes should be flagged as lower confidence.

Temporal resolution: At ~6–12 day revisit, you cannot track individual vessel movements. This is fleet-level statistical monitoring, not real-time tracking.

Integration with AIS Data

SAR ship detection is most powerful when combined with AIS data, not as a replacement for it:

• AIS tells you where ships are and their identities
• SAR tells you how many ships are there, including non-reporting vessels

The combination reveals the "dark fraction" — the proportion of activity invisible to AIS alone. This combined approach is the basis for the most advanced maritime domain awareness systems used by coast guards and fisheries enforcement agencies.

Summary

Continuous SAR ship detection monitoring builds a time series of vessel counts for any defined ocean area, independent of AIS transponder data. Sentinel-1's regular 6–12 day revisit cycle captures weekly and seasonal patterns in maritime activity, reveals dark vessels that do not appear in AIS records, and can detect sudden disruptions to normal traffic patterns. The key operational consideration is coverage compensation — always accounting for the fraction of your monitoring area actually imaged in each satellite pass before interpreting vessel count trends.