Abstract
The purpose of this study is to estimate the surface roughness (rms) using
TerraSar-X data in HH polarization. Simulation of data is carried out at a wide range of
moisture and roughness using the Integral Equation Model (IEM). The inversion method
is based on Multi-Layer Perceptron neural network. Inversion technique is performed in
two steps. In the first step, the neural network is trained using synthetic data. The inputs of
the first neural network are the backscattering coefficient and incidence angle, and the
moisture is the output. In the next step, three neural networks are built based on a prior
and without prior information on roughness. The inputs of three neural network are
backscattering coefficient, estimated moisture in the first step and incidence angle and the
roughness is output. The validation of the proposed methods is carried out based on
synthetic and real data. Ground roughness measurements are extracted from Digital
Terrain Model (DTM) using the fractal method. The accuracy of moisture from synthetic
data is 6.1 vol. % without prior information on moisture and roughness. The roughness
(rms) accuracy of synthetic datasets is 0. 61cm without prior information and is 0.31cm
and 0.38cm for rms lower than 2cm and rms between 2 and 4 cm, with prior information
on roughness. The result's analysis of the simulated data showed that the prior information
on roughness strongly improves the accuracy of roughness and moisture estimates. The
accuracy of rms estimates for the TerraSar-X image in the HH polarization is about 0.9 cm
in the case of no prior information on roughness. The accuracy improves to 0.57cm for
rms lower than 2cm and 0.54cm for rms between 2 and 4 cm with prior information on
roughness. An overestimation of rms for rms lower than 2cm and an underestimation of
rms for rms higher than 2cm are observed. The results of the accuracy of the synthetic and
real data showed that the X band in HH polarization has a very good potential to estimate
the soil roughness.