SARpolarizationsentinel-1technical

VV, VH, HH, HV: SAR Polarization Demystified

Kazushi MotomuraJanuary 31, 20265 min read
VV, VH, HH, HV: SAR Polarization Demystified

Quick Answer: SAR polarization refers to the orientation of the transmitted and received electromagnetic wave. VV emphasizes surface scattering (good for water, ice, and soil), while VH captures volume scattering from complex structures like vegetation. Sentinel-1 typically provides both VV and VH in its standard IW mode. Choosing the right polarization is often more impactful than choosing the right algorithm.

What Polarization Actually Means

Electromagnetic waves oscillate in a specific plane. When we say a SAR system transmits in "V" (vertical) polarization, we mean the electric field oscillates perpendicular to the ground. "H" (horizontal) means it oscillates parallel to the ground.

The naming convention uses two letters: transmit first, receive second.

  • VV: Transmit vertical, receive vertical
  • VH: Transmit vertical, receive horizontal
  • HH: Transmit horizontal, receive horizontal
  • HV: Transmit horizontal, receive vertical

VV and HH are called "co-polarized" (same polarization transmitted and received). VH and HV are "cross-polarized" (the received polarization differs from transmitted).

Why does this matter? Because different surface types interact differently with each polarization, and the choice directly affects what information you can extract.

The Scattering Mechanisms

Three primary scattering mechanisms dominate SAR imagery, and each has a characteristic polarimetric signature:

Surface Scattering

Occurs at relatively smooth surfaces — bare soil, water, roads. The radar bounces off the surface in a predictable way, with most energy remaining in the co-polarized channel.

Dominant in: VV or HH Examples: Calm water, bare soil, paved surfaces

Volume Scattering

Occurs within complex, three-dimensional structures where the radar signal bounces multiple times between elements — tree branches, leaves, crop canopies. Each bounce can change the polarization, so significant energy appears in the cross-polarized channel.

Dominant in: VH or HV Examples: Forest canopy, dense crops, snow pack

Double-Bounce Scattering

The radar signal bounces off a horizontal surface, hits a vertical surface (or vice versa), and returns to the satellite. This creates a phase shift that produces a very strong co-polarized return.

Dominant in: HH (primarily) Examples: Buildings next to roads, tree trunks standing in water (flooded forest)

Sentinel-1: VV + VH

Sentinel-1 operates in C-band (5.405 GHz) and its standard Interferometric Wide (IW) swath mode provides dual-polarization data: VV and VH simultaneously.

This is a practical compromise. Fully polarimetric systems (transmitting and receiving all four combinations) provide richer information but require twice the data rate and more complex processing. Sentinel-1's VV+VH dual-pol covers the most useful polarimetric space for its primary applications.

When to use VV

Water and flood mapping: Calm water produces almost no backscatter in any polarization, but the contrast between water and land is generally highest in VV. When I'm delineating a flood boundary, VV is almost always my first choice.

Sea ice classification: The distinction between different ice types (first-year, multi-year, thin ice) is often clearest in VV due to surface scattering differences.

Soil moisture estimation: Surface scattering from bare or sparsely vegetated soil is better characterized by VV.

When to use VH

Forest monitoring: VH is more sensitive to canopy structure because volume scattering from branches and leaves produces cross-polarized returns. For biomass estimation or deforestation detection, VH typically outperforms VV.

Crop classification: Different crop types at different growth stages produce distinct VH signatures. Rice paddies, for instance, show a characteristic VH increase as the crop develops from standing water through tillering to heading.

Separating agriculture from forest: In VV, both forests and mature crops can appear similar. In VH, forests generally show higher backscatter due to their more complex three-dimensional structure.

When to use the VH/VV ratio

The cross-pol ratio (VH/VV, often expressed in dB) is itself a useful indicator. It's sensitive to vegetation characteristics while being partially normalized against some systematic effects like incidence angle variation.

A high VH/VV ratio suggests dominant volume scattering (dense vegetation). A low ratio suggests surface scattering (bare ground, water).

L-Band: A Different Game

SAR Frequency Bands and Applications — longer wavelengths penetrate deeper into vegetation, with L-band (NISAR) reaching branches and trunks that C-band (Sentinel-1) cannot

When NISAR begins providing L-band data, the polarimetric picture changes significantly. L-band's longer wavelength (23.5 cm vs. 5.6 cm for C-band) penetrates deeper into vegetation canopy, interacting with larger structural elements — branches and trunks rather than leaves.

This means L-band HH is particularly sensitive to double-bounce from trunk-ground interactions, making it excellent for:

  • Detecting flooded forest (the trunk-water double bounce is very strong in HH)
  • Estimating above-ground biomass (trunk size correlates with biomass)
  • Mapping wetland inundation under canopy

NISAR will provide dual-pol L-band (HH+HV) and single-pol S-band data, opening up analysis possibilities that C-band VV+VH simply cannot address.

Practical Recommendations

For most users starting with Sentinel-1:

  1. Begin with VV for general-purpose visualization — it provides the most intuitive contrast between land and water
  2. Switch to VH when working with vegetated areas — forest, agriculture, wetlands
  3. Try creating an RGB composite with VV (red), VH (green), and VV/VH (blue) for a false-color image that separates scattering mechanisms visually
  4. Don't default to one polarization for everything — the 30 seconds it takes to check both can save hours of misinterpretation

Load both VV and VH data for the same scene and toggle between them. The differences tell you about the surface properties that each polarization is sensitive to.

Kazushi Motomura

Kazushi Motomura

Remote sensing specialist with 10+ years in satellite data processing. Founder of Off-Nadir Lab. Master's in Satellite Oceanography (Kyushu University).