Backscattering Coefficients Modeling for Congo Rainforest in Microwave Remote Sensing

Modeling rainforest backward scattering coefficient is an important research area in microwave remote sensing. We have developed a theoretical model for rainforest backscattering coefficients, incorporating three key backscatter contributions, to serve as a reference for in-orbit satellite calibration. By explicitly correlating the backscatter with the incident angle, the proposed model provides clear physical significance and facilitates the retrieval of geophysical parameters. Using data-driven fitting and validation against satellite measurements, we demonstrate the model's simplicity, accuracy, and stability, underscoring its practical value for external calibration applications.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/iwrfat65352.2025.11103321
Primary Topic
Soil Moisture and Remote Sensing
Type
article
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Backscattering Coefficients Modeling for Congo Rainforest in Microwave Remote Sensing

Jian Shang, Yutong Liu, Weidong Hu, Yuming Bai et al.
Soil Moisture and Remote Sensing
article

Backscattering Coefficients Modeling for Congo Rainforest in Microwave Remote Sensing

Jian Shang, Yutong Liu, Weidong Hu, Yuming Bai, Jiale Guo, Minghao Feng
article en

Abstract

Modeling rainforest backward scattering coefficient is an important research area in microwave remote sensing. We have developed a theoretical model for rainforest backscattering coefficients, incorporating three key backscatter contributions, to serve as a reference for in-orbit satellite calibration. By explicitly correlating the backscatter with the incident angle, the proposed model provides clear physical significance and facilitates the retrieval of geophysical parameters. Using data-driven fitting and validation against satellite measurements, we demonstrate the model's simplicity, accuracy, and stability, underscoring its practical value for external calibration applications.

Beijing Institute of Technology (CN), Beijing University of Posts and Telecommunications (CN), China Meteorological Administration (CN), Tangshan College (CN), National Institute of Metrology (CN)
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Openalex Percentile: Top 81%
Soil Moisture and Remote Sensing
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