Using an EM Model in Remote Sensing of Soil Surface from Polarimetric SAR
K.S.Chen1, M.K.Tsay2 and T.D. Wu2
1Center for Space and Remote Sensing Research
2 Department of Electrical Engineering
National Central University
Chung - li, Taiwan
E-mail : dkschen@csrsr.ncu.edu.tw
Abstract
In this paper, we apply an em model to retrieve the soil surface parameters from the polarimetric SAR imagery data . Treating the soil surface as a dielectric layer of finite depth, followed by calculation of reflection coefficients from such layer , the IEM model was used to account for the presence of layer medium representing the soil dielectric profile. Model bebavior was numerically illustrated as function of roughness, moisture content, and a dielectric change rate . Result, when compared with available ground truth shows that the presented approach gives fairly good agreement. The only free parameter, the dielectric change rate , yet remains to be further determined experimentally .
Introduction
Soil surface information is the most important factors related to our environmental change . Much efforts have been devoted to better estimation of moisture content by means of remote sensing. As high spatial resolution is required, synthetic aperture radar (SAR) might be a suitable candidate.The complexity of wave-target process, however ,imposes some restrictions when dealing with the parameters retrieval from acquired SAR data . For example, within an imaging resolution cell, the total return composes of many different sources being observed these contributions differ in their different behavior of wave propagation and scattering and some of them are the dominant ones depending on radar wavelengths and other factors. The random wave traveling path within resolution cell inherently produces speckle noise making the measurement uncertainty even larger. Then the inverse mapping is highly nonlinear and analytic function usually does not exit. As the searching of better solution continues, this paper proposed an effective method. A practical soil scattering model based on the framework of IEM model is developed. The model is then used to generate a set of well-controlled Synthetic data for later training data. A target decomposition was applied to preprocess the SAR data acquired during SIR-C mission the soil extract the soil surface contribution. Finally, a neural network trained by Kalman filtering technique was served as inverse mapper. The test data is a set of SIR-C polarimetric SAR imagery data over Washita , Oklahoma USA. It was demonstrated that the proposed technique gives a better agreement between inverted data and ground truth.
EM Scattering from An inhomohenous soil surface
Most of the theoretical models treat the soil surface as a homogenous half- space.
This usually is not so. For example , soil surfaces dry up from the top down after rain forming a dielectric profile in which the permittivity varies in depth. This it is desirable to incorporate a physical
Dielectric gradient into our theoretical modeling . The necessary step to extend the IEM model ( fung et al ,1992,chen et al , 1995 )to incorporate a vertical soil moisture profile to replace the standard reflection coefficients by a layered reflection coefficients. Consider the transitional dielectric layer given by (Brekhovskikh , 1980)in which the pemittivity as function of depth, z,is
| e(z) = 1 + er1 |
exp(mz)
----------------
1 + exp(mz) |
(1) |
In the formulation of Brekhovskikh, note that
e
r1=er¥-1, and - N =
e r1. The inputs to the model are the transition rate factor m and the dielectric constant at =
¥ whichis
e¥.The real part of the dielectric will always start at
-¥= which is air and gradually change to
e ¥ at rate of m. The Fresnel reflection coefficient are given by.
To begin with the model simulation , we first show the dependence of backscattering coefficient on m, the change rate , The value of m clearly depends on the soil type , among other. At normal incidence, there is polarization dependence , i.e,
s°
hh¹s°
vv. There is a cross- over at incidence angle of 60 degrees for the case of m=10 and m=100. Fig 1 illustrates the dependence of backscattering coefficient on m.
Fig.1 Dependence of the backscattering coefficient on m
