Overview

SAR is the abbreviation for Synthetic Aperture Radar. It is a common type of active Earth observation imagery that leverages microwave radar to generate data. This means that a SAR satellite transmits radar pulses toward the ground and measures the energy that bounces back to the source. The SAR system processes the returned signal, which is called backscatter, to form an image of the surface. SAR does not depend on weather or daylight and can capture the target area in almost all conditions, relying on its own signal.

When SAR Imagery Is Useful

Considering its unique features, SAR is useful in several circumstances where other types, such as optical sensors, cannot provide the required imagery. For example:

• Cloudy or rainy weather
• Nighttime monitoring
• Accurately identifying surface roughness and moisture variation
• urban and infrastructure deformation monitoring

SAR satellites help users monitor and manage crises and disasters, and support agricultural and forestry activities. Whenever reliable, repeatable monitoring is needed under clouds or at night, SAR imagery is essential. Moreover, if structural and moisture-related properties are more relevant than visible reflectance, SAR becomes the preferred source.

How SAR Works

The SAR satellite sends radar pulses toward the target area and measures how long they take to return and how strong the backscatter (the returned signal) is. This is the core principle of SAR imaging. The onboard antenna is physically limited in size. Therefore, SAR systems use the satellite’s motion to synthesize a larger antenna. This principle, called synthetic aperture, enables SAR to achieve high resolution along the flight direction (azimuth). SAR captures images of the surface at an angle rather than straight down, resulting in side-looking imagery. This feature creates two key dimensions:

• Range: Distance from the sensor
• Azimuth: Along the satellite track

The angle of the capture affects the image brightness, shadows, and geometric distortions.

Key Acquisition Parameters

These acquisition parameters play significant roles in shaping the SAR image:

• Frequency band (X, C, L): Controls penetration depth and sensitivity to surface roughness and moisture.
• Polarization (VV, VH, HH, HV): Provides detailed information about the target area’s structure and vegetation.
• Incidence angle: Influences brightness, shadowing, and layover.
• Orbit and revisit time: Determine how often the area can be monitored in a given time frame.

SAR Acquisition Geometry

Since SAR satellites capture images from the side, the incidence angle (the angle between the radar beam and the ground) typically affects how features appear in the resulting image. Steeper angles often yield brighter returns, but with greater geometric distortion. Conversely, shallower angles create smoother, darker areas in the image. Terrain enhances these geometric effects and creates distortions unique to SAR:

• Foreshortening: Slopes facing the radar appear compressed.
• Layover: Tall objects lean toward the sensor.
• Radar shadow: Areas behind tall features receive no signal and appear dark.

This is why mountains and urban areas vary significantly from flat terrain in SAR. Thus, to properly interpret SAR imagery for different purposes, understanding this geometry and unique distortions is necessary.

Radiometry and Calibration

The raw pixel values captured by the SAR satellite must be converted to calibrated backscatter to derive physically interpretable values. This procedure enables the calibrated data to describe how much radar energy the surface reflects, allowing reliable comparisons across scenes, dates, and sensors. SAR backscatter is commonly expressed as:

• σ⁰ (sigma-naught): Backscatter normalized per unit ground area; the most common product for general analysis.
• γ⁰ (gamma-naught): Further normalized for terrain slope; preferred in mountainous areas.
• β⁰ (beta-naught): Backscatter relative to slant-range geometry; mostly used internally during processing.

Terrain correction and incidence-angle normalization remove most geometric effects. This results in measurements tied to actual surface properties, rather than the viewing geometry. The procedure is crucial for change detection, time-series monitoring, and analysis where values need to be physically meaningful.

Common SAR Artefacts and Noise

SAR imagery is subject to three major types of noise and artefacts:

Speckle

Due to constructive and destructive interference of radar returns, SAR images may be grainy. This leads to speckle, a type of coherent noise that can be reduced by multi-looking, speckle filters, or time averaging.

Thermal Noise and Border Effects

All sensors are prone to the noise floor, a weak background signal that appears as faint grain or banding, particularly in smooth areas such as water and snow. Furthermore, some SAR products exhibit bright or dark borders, caused by how the sensor sweeps or by noise removal during processing.

Geolocation and Terrain Effects

If the SAR image is collected in slant-range geometry (distance from the sensor), the raw slant-range SAR image will not be map-aligned. Complex terrain distorts features, causing misalignments when compared with optical imagery. Terrain correction, or orthorectification, addresses this issue by using the DEM to reposition each pixel to match actual coordinates.

SAR Imagery Limitations

SAR imagery does not resemble natural-color imagery, and brightness depends on acquisition geometry, polarizations, and surface properties. This makes interpreting SAR imagery complex. Inexperienced users often struggle to interpret the processed SAR imagery correctly. Common noises, such as speckle, can also obscure the visibility of fine features and objects, or make their shape harder to discern, especially in mountains or urban areas. To overcome these challenges, accurate calibration, terrain correction, and incidence-angle normalization are required. This increases the processing workload, as these actions require specialized tools and expertise. Acquiring high-resolution SAR creates large files, and modes like Spotlight and Stripmap are difficult to handle due to their sizes and storage demands.

Conclusion

SAR sensors can capture data in all weather conditions, day and night. The signals these satellites send and receive are sensitive to physical properties, allowing them to deliver high-resolution information about the surfaces they reflect off. This makes SAR imagery well-suited for several applications where users cannot depend on natural light. Related article: Optical Imagery