Sensors & Physics

SAR Fundamentals

How synthetic aperture radar sees through clouds, in darkness, and reveals what optical sensors cannot

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Synthetic Aperture Radar (SAR) is a radar imaging system carried on aircraft or satellites that creates high-resolution images of the Earth's surface by emitting microwave pulses and measuring what bounces back. Unlike optical sensors that rely on sunlight, SAR generates its own illumination — so it works at night. Unlike optical sensors that are blocked by clouds, SAR's microwave frequencies pass through clouds, rain, and smoke. SAR does not see color or reflected light. It sees surface roughness, moisture content, and geometric structure. This makes it essential for flood mapping, deforestation monitoring, ground deformation measurement, and any application where persistent, all-weather observation is required.

Why It Matters

Approximately 67% of the Earth's land surface is cloud-covered at any given time, with tropical regions exceeding 80% average cloud cover. For optical sensors like Sentinel-2 and Landsat, clouds are opaque walls. Every cloudy pixel is a gap in coverage — a day, a week, sometimes months of missing data over persistently cloudy regions.

SAR does not have this problem. Microwave radiation at C-band (5.4 GHz, ~5.6 cm wavelength) passes through clouds as if they are not there. It passes through light rain. It passes through smoke from wildfires. It operates identically day and night because it carries its own transmitter.

For monitoring floods — events that are by definition associated with cloud cover and precipitation — SAR is not just useful. It is often the only sensor that can observe what is happening. For monitoring tropical deforestation in regions with near-permanent cloud cover, SAR provides the continuity that optical sensors structurally cannot.

But SAR does more than fill gaps in optical coverage. It measures fundamentally different properties of the surface. Where an optical sensor measures how a surface reflects sunlight (its color, essentially), SAR measures how a surface scatters microwave energy — which is determined by surface roughness, dielectric properties (related to moisture content), and geometric structure. This makes SAR sensitive to things that are invisible to optical sensors: soil moisture beneath vegetation canopies, the structural difference between intact forest and logged forest, millimeter-scale ground deformation over time.

Fabric Connection

Fabric processes SAR data as part of its multi-sensor harmonization pipeline. When Fabric executes a flood detection pattern, it handles the SAR-specific processing chain: speckle filtering, radiometric terrain correction, backscatter thresholding, and integration with optical and elevation data.

The key challenge Fabric addresses is composability: making SAR-derived observations combinable with optical observations in a common analysis-ready framework. A SAR backscatter measurement and a multispectral reflectance measurement are fundamentally different types of claims about the same location. Observational Grammar provides the framework for combining them: each is a claim with its own physics, its own uncertainty, and its own provenance. The composite evidence is stronger than either alone — but only if the architecture preserves the distinction between what each sensor actually measured.

References

Moreira, A., et al. (2013). "A Tutorial on Synthetic Aperture Radar." IEEE Geoscience and Remote Sensing Magazine, 1(1), 6-43. https://doi.org/10.1109/MGRS.2013.2248301
Torres, R., et al. (2012). "GMES Sentinel-1 Mission." Remote Sensing of Environment, 120, 9-24. https://doi.org/10.1016/j.rse.2011.05.028
Lee, J.S. & Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications. Boca Raton: CRC Press.
Woodhouse, I.H. (2005). Introduction to Microwave Remote Sensing. Boca Raton: CRC Press.
ESA (2024). "Sentinel-1 SAR User Guide." European Space Agency. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar