PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
July 17, 2013IEEE Transactions on Geoscience and Remote Sensing104 citations

Double-Bounce Component in Cross-Polarimetric SAR From a New Scattering Target Decomposition

View Full Paper
SHSang‐Hoon HongSWShimon Wdowinski

Key Points

Key points are not available for this paper at this time.

Abstract

Common vegetation scattering theories assume that the synthetic aperture radar (SAR) cross-polarization (cross-pol) signal represents solely volume scattering. We found that this assumption is incorrect based on SAR phase measurements acquired over the South Florida Everglades indicating that the cross-pol radar signal often samples the water surface beneath the vegetation. Based on these new observations, we propose that the cross-pol signal consists of both double-bounce and volume scattering components. The simplest multibounce scattering mechanism that generates cross-pol signal occurs by rotated dihedrals. Thus, we use the rotated dihedral mechanism to revise some of the vegetation scattering theories and develop a fourcomponent decomposition algorithm with single-bounce, co-pol double-bounce, cross-pol double-bounce, and volume scattering components. We tested the new decomposition in both urban and rural environments using RADARSAT-2 quad-pol data sets. The decomposition of the San Francisco area shows higher doublebounce scattering and reduced volume scattering in the urban area with respect to the common three-component decomposition. The decomposition of the rural Everglades area shows that the relation between volume and cross-pol double bounce depends on the vegetation density. Thus, we suggest that, when possible, SAR-based biomass estimate studies should use the volume scattering calculated by our decomposition rather than the cross-pol signal, which also contains a double-bounce component.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Hong et al. (2013) studied this question.

synapsesocial.com/papers/6a18fd831d64db491ad30b6bhttps://doi.org/10.1109/tgrs.2013.2268853
Ask AI
Helpful
Bookmark
Share
View Full Paper