Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Abstract

Abstract:Microwave remote sensing can measure surface geometry. Via the processing of the Synthetic Aperture Radar (SAR) data, the earth surface geometric parameters can be provided for geoscientific studies, especially in geological mapping. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering. Of the available models, the Integral Equation Model (IEM) for co-polarized data has been the most frequently used model. Therefore, by the processing of the SAR data using this model, the surface geometry can be studied. In the IEM, the surface roughness geometry is calculable via the height statistical parameter, the rms-height. However, this parameter is not capable enough to represent surface morphology, since it only measures the surface roughness in the vertical direction, while the roughness dispersion on the surface is not included. In this paper, using the random fractal geometry capability, via the implementation of the power-law roughness spectrum, the precision and correctness of the surface roughness estimation has been improved by up to 10%. Therefore, the random fractal geometry is implemented through the calculation of the input geometric parameters of the IEM using the power-law surface spectrum and the spectral slope. In this paper, the in situ roughness measurement data, as well as SAR images at frequencies of L, C, and X, have been used to implement and evaluate the proposed method. Surface roughness, according to the operational frequencies, exhibits a fractal or a diffractal behavior.

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

“…This is in accordance with several previous studies which have found that, for agricultural surfaces, the exponential function provides the best match between predicted and SAR backscatters [82][83][84][85]. Generally speaking, in spite of the slight underestimation, one can state that the simulations of IEM by using the measured L are very satisfying with respect to what have been reported in previous works [20,67,80,81,86]. However, measuring the correlation length is a problematic task because of its dependence on the profile length, as well as the standard deviation of surface height (Hrms) [86], and thus its is considered as the most difficult parameter to be measured at the field with a good accuracy.…”

“…Generally speaking, in spite of the slight underestimation, one can state that the simulations of IEM by using the measured L are very satisfying with respect to what have been reported in previous works [20,67,80,81,86]. However, measuring the correlation length is a problematic task because of its dependence on the profile length, as well as the standard deviation of surface height (Hrms) [86], and thus its is considered as the most difficult parameter to be measured at the field with a good accuracy. In the same vein, several studies have reported that the discrepancies between backscattering coefficients simulated by IEM and those measured by SAR sensors are mainly related to the value of L measured [87,88].…”

Numerical Analysis of Radar Response to Snow Using Multiple Backscattering Measurements for Snow Depth Retrieval

“…The I 2 EM is applicable on a wide range of surfaces, from smooth to rough where k 0 s < 3 for a certain radar wavenumber. The co-polarized backscattering coefficient equation according to [4,10,11]:…”

“…and pp is either the hh or vv polarizations, and W (n) is the Fourier transform of nth power of the autocorrelation function (ACF) of the rough surface [10]. f hh , f vv , F hh , and F vv are approximated by the following equations:…”

Editorial for Special Issue “Advances in SAR: Sensors, Methodologies, and Applications”

“…Feature detection from SAR images is another major topic in Microwave Remote Sensing. Ghafouri et al [17] present a method to better estimate IEM (Integral Equation Model) input parameters for multi-frequency SAR data. Di Martino et al [18] describe the role of resolution for the estimation of fractal dimension maps.…”