Reconstruction of the Angular Dependence of the Sea Ice Backscattering Pattern According to the GNSS-R Data
Year: 2025
Type: article
Abstract: This study addresses the challenge of understanding sea ice microwave scattering properties by developing a novel method to retrieve scattering indicatrix and angular dependence of backscattering patterns from Global Navigation Satellite System Reflectometry (GNSS-R) data. Sea ice remote sensing in quasi-specular reflection area requires theoretical models validated using experimental data across different frequency bands. However, only limited observations of quasispecular scattering in the L-band exist. We developed an algorithm to convert Doppler spectra from delay-Doppler maps (DDM) obtained by the TDS-1 satellite into scattering indicatrix and then to angular dependence of the sea ice backscattering patterns, using geometric transformations from bistatic sensing geometry. The method was applied to approximately 100 DDM measurements over the Sea of Okhotsk during February–March 2017, where ice concentration remained at 90–100% with temperatures below 0 °C. Results show that L-band (19 cm wavelength) exhibits broader angular dependence compared to Ka-band (8.45 mm) and Ku-band (2.21 cm) measurements from dual-frequency precipitation radar (DPR), contrary to expectations based solely on surface roughness. This anomalous broadening is attributed to volume scattering effects within sea ice, where L-band signals penetrate up to one meter depth compared to millimeter-scale penetration in Ka- and Kufrequency bands. The findings provide new insights into L-band scattering mechanisms and offer a validated approach for improving theoretical models of sea ice microwave interaction.
DOI: 10.2205/2025es001082
Authors: Dmitriy Kovaldov, Vladimir Karaev, Yuriy Titchenko, Vyacheslav Fateev, Vladislav Lopatin, Feixiong Huang, Xiaoli Wang, Jie Guo
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: diamond
Funder: Russian Science Foundation
OpenAlex ID: W7108079141