Surface Parameter Bias Disturbance in Radar Backscattering From Bare Soil Surfaces

Surface parameters (roughness and soil permittivity) are crucial for characterizing backscattering from bare soil. However, the estimation of roughness parameters (root-mean-square (rms) height and correlation length) depends on the sample surface size. The conversion between dielectric constant and...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 17
Main Authors: Wang, Zhihua, Yang, Ying, Zeng, Jiangyuan, Chen, Kun-Shan
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Backscatter
Backscattering
Backscattering coefficient estimation
bare soil surfaces
Bias
Dielectric constant
Dielectrics
Estimation
estimation bias
Integral equations
Moisture content
Parameter estimation
Parameters
Permittivity
Radar
Rough surfaces
sample surface size
SAR (radar)
Soil
Soil analysis
Soil moisture
Soil surfaces
Statistical models
Surface roughness
Synthetic aperture radar
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Surface parameters (roughness and soil permittivity) are crucial for characterizing backscattering from bare soil. However, the estimation of roughness parameters (root-mean-square (rms) height and correlation length) depends on the sample surface size. The conversion between dielectric constant and soil moisture is disturbed by the dielectric model. These estimation biases significantly compromise the reliability of backscattering coefficients derived from analytic modeling, numerical simulations, and experimental measurements. In this study, we illustrate the statistical relationship between sample surface size and estimation bias of surface roughness at varied accuracy levels. To quantify the estimation bias of surface roughness with sample surface size and the estimation bias of soil permittivity, we analyze the propagation from the estimation bias of surface parameters to the backscattering coefficient error by the advanced integral equation model (AIEM) model. By comparing it with measurement data, we quantitatively confirm the impact of roughness parameter estimation bias. Ultimately, quantifying the backscattering coefficient error as a function of sample surface size and incident angle allows for selecting the optimal sample surface sizes suitable for L-band synthetic aperture radar (SAR) simulation and soil moisture retrieval, along with their applicability over various incident angles. This study suggests sample surface sizes for estimating roughness parameters and backscattering coefficients at various levels of accuracy.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3439520