Solid Angle Geometry-Based Modeling of Volume Scattering with Application in the Adaptive Decomposition of GF-3 Data of Sea Ice in Antarctica
Abstract
:1. Introduction
2. Coherent Scattering Modeling for a 3D Oriented Ellipsoidal Particle
2.1. Scattering Matrix for a 3D Oriented Ellipsoidal Particle
2.2. Coherency Matrix for a 3D Oriented Spheroidal Particle
3. PDFs Modeling for 3D Uniformly Oriented Ellipsoidal Particles
3.1. Solid Angle
3.2. PDFs
4. Volume Scattering Modeling for 3D Uniformly Distributed Spheroidal Particles
4.1. Multiplicative Volume Scattering Model
4.2. Special Scenerios
4.3. Simulation
- (1)
- Sign of is related to the shape and orientation of particles but independent of incidence .
- (2)
- Entropy is influenced by particle shape and orientation; of prolates is higher than that of oblates.
- (3)
- The scattering angle is also determined by particle shape and orientation; of prolates is higher than that of oblates. of prolates with vertical orientation is higher than that of prolates with horizontal orientation. Moreover, of oblates is more easily affected by the incidence . The - variation becomes more significant as the mean tilt angle increases.
5. Adaptive Polarimetric Decomposition of Volume Scattering Component for Sea Ice
5.1. Scattering Models for Decomposition
- (1)
- (2)
- (3)
- The brine inclusions are approximated by dipoles, as the size of brine inclusions in sea ice is usually at millimeter or even submillimeter level, smaller than the working wavelength of the existing spaceborne PolSAR systems [65,66,67]. As a result, , i.e., , and is typically 1, without loss of generality.
5.2. Decomposition Method
5.3. Transmission Effect
6. Decomposition Experiment on GF-3 PolSAR Data of Antarctica
6.1. Dataset in Prydz Bay
6.2. Results and Analysis
6.3. Discussion
- (1)
- Take the extinction effects into the developed volume models and decomposition to achieve an estimation of the thickness of sea ice.
- (2)
- Retrieve the surface and double-bounce scattering components after the volume decomposition to distinguish different types of sea ice.
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
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Li, D.; Lu, H.; Zhang, Y. Solid Angle Geometry-Based Modeling of Volume Scattering with Application in the Adaptive Decomposition of GF-3 Data of Sea Ice in Antarctica. Remote Sens. 2023, 15, 3208. https://doi.org/10.3390/rs15123208
Li D, Lu H, Zhang Y. Solid Angle Geometry-Based Modeling of Volume Scattering with Application in the Adaptive Decomposition of GF-3 Data of Sea Ice in Antarctica. Remote Sensing. 2023; 15(12):3208. https://doi.org/10.3390/rs15123208
Chicago/Turabian StyleLi, Dong, He Lu, and Yunhua Zhang. 2023. "Solid Angle Geometry-Based Modeling of Volume Scattering with Application in the Adaptive Decomposition of GF-3 Data of Sea Ice in Antarctica" Remote Sensing 15, no. 12: 3208. https://doi.org/10.3390/rs15123208
APA StyleLi, D., Lu, H., & Zhang, Y. (2023). Solid Angle Geometry-Based Modeling of Volume Scattering with Application in the Adaptive Decomposition of GF-3 Data of Sea Ice in Antarctica. Remote Sensing, 15(12), 3208. https://doi.org/10.3390/rs15123208