Forward simulation of thin ice backscatter mechanism in the Bohai Sea region based on the MSIB and SMRT coupling model
Accurate modeling of Synthetic Aperture Radar (SAR) backscattering interactions with sea ice is essential for advancing sea ice remote sensing. However, systematic research employing forward modeling to specifically examine how different sea ice physical properties influence backscattering remains l...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2515538 |
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| Summary: | Accurate modeling of Synthetic Aperture Radar (SAR) backscattering interactions with sea ice is essential for advancing sea ice remote sensing. However, systematic research employing forward modeling to specifically examine how different sea ice physical properties influence backscattering remains limited. This study introduces the MSIB-SMRT model, a novel framework coupling the Snow Microwave Radiative Transfer (SMRT) model with the Multilayer Snow and Ice Microwave Backscatter (MSIB) model. Utilizing 2024 field measurements of thin ice samples from China’s Bohai Sea region, the study investigates how SAR backscattering responds to factors such as ice thickness, salinity, bubble radius, density, temperature, and snow-ice interface roughness under varying simulated observation scenarios. Results indicate that the absolute error between observed and simulated backscattering is less than 3.63 dB. The findings reveal that the root mean square height of the snow-ice interface exerts the most significant impact on backscattering, followed by the correlation length of the snow-ice interface, temperature, and salinity, while thickness, density, and other factors have comparatively weaker effects. This work validates the MSIB-SMRT model using satellite SAR data and provides a reliable approach for studying sea ice backscatter mechanisms, offering new insights for future sea ice parameter retrieval. |
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| ISSN: | 1753-8947 1753-8955 |