Polarimetric Synthetic Aperture Radar Applications in Forest Structure Estimation
Summary
Polarimetric Synthetic Aperture Radar (PolSAR) has emerged as a pivotal tool for characterising three-dimensional forest structure at regional to global scales. By exploiting multiple polarisation channels, PolSAR systems can disentangle scattering contributions from canopy, branches and underlying ground. Interferometric and tomographic extensions further enable the retrieval of canopy height, vertical foliage distribution and above-ground biomass through coherence and phase-difference measurements. These capabilities are largely independent of weather and illumination conditions, making PolSAR especially valuable for monitoring remote or tropical regions under persistent cloud cover. Recent advances in sensor design, data processing algorithms and integration with complementary datasets promise improved accuracy and computational efficiency. As forest ecosystems play a critical role in the global carbon cycle, robust estimation of structural parameters via PolSAR supports climate modelling, carbon accounting and sustainable forest management worldwide.
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Polarimetric Synthetic Aperture Radar Applications in Forest Structure Estimation publication trend
The graph below shows the total number of articles in polarimetric synthetic aperture radar applications in forest structure estimation across all publications each year (not limited to Nature Index journals).
Technical terms
Polarimetric Synthetic Aperture Radar (PolSAR): An active microwave remote-sensing technique recording backscatter in multiple polarisation channels to infer target geometry and composition.
Interferometric Coherence: A measure of similarity between two SAR acquisitions used to detect changes in phase caused by vertical forest structure.
Random Volume over Ground (RVoG) model: A semi-empirical framework representing forest canopy as a uniform volume scatterer above a ground reflector for height retrieval.
Tomographic SAR (TomoSAR): An extension of SAR interferometry that reconstructs three-dimensional reflectivity profiles by exploiting multiple observation baselines.
Lidar: A laser-based active sensor that provides precise three-dimensional point clouds of forest canopy and terrain elevations.
References
- A Method for Forest Canopy Height Inversion Based on UAVSAR and Fourier–Legendre Polynomial—Performance in Different Forest Types. Drones (2023).
- Large-Scale Forest Height Mapping by Combining TanDEM-X and GEDI Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023).
- Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing (2021).
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