Introduction
During recent years, theoretical modeling and field experiments have established the fundamentals of active microwave remote sensing as an important tool in determining physical properties of soil. The ability to estimate soil moisture in the surface layer to an approximately 5 cm depth by microwave remote sensing has been demonstrated under a variety of the topographic and land cover conditions (Engman et al., 1995). Despite the promise, its application to hydrological and agricultural sciences has been hampered by natural variability and the complexity of the vegetation canopy and surface roughness that significantly affect the sensitivity of radar backscattering to soil moisture.
Although experiments demonstrated radar sensitivity to soil moisture variation more than two decades ago (Ulaby, 1974), an operational algorithm for mapping soil moisture distribution has not been developed because of the effects of the surface roughness and vegetation cover. The inversion of soil moisture information from radar backscatter became more rigorous after the availability of multi-polarization radar data. Several algorithms have been developed for measuring bare surface soil moisture quantitatively using either dual-polarization L-band SAR image data (Dubois et al., 1995, Ulaby et al., 1996 and Shi et al., 1997) or three-polarization SAR measurements (Oh et al., 1992). The algorithms had been applied to a series of L-band SIR-C and JPL/AIRSAR image data successfully by using VV and HH polarization (Dubois 1995 and Shi 1997). However, all those algorithms were under the surface scattering consideration. They used the weighted combinations of the different polarization signatures to minimize the effect of surface roughness so that soil moisture can be directly inferred from SAR image data. The effect of vegetation cover has not been included in current available algorithms. It is clear that vegetation cover will cause an under-estimation of soil moisture and an over-estimation of surface roughness when we apply the algorithm for bare surface soil moisture estimation to vegetation covered regions.
A polarimetric SAR backscatter measurements, by using eigenvalues and eigenvectors of the covariance matrix, can be decomposed to into three components based on the scattering types - (1) an odd number of reflections, (2) an even number reflections, and (3) a cross-polarized scattering power [Van]. It can be written as
Where
l and
K are eigenvalues and eigenvectors.
* is a conjugate operator for a complex number and T is a transpose operator for a vector. The subscript 1, 2, and 3 represents the decomposed odd, even, and defuse components, respectively. This decomposition technique allows us to obtain the estimation of single and double reflection components of backscattering coefficients for VV and HH polarization.
In this study, we evaluate the usage of the decomposition theory in application of estimating soil moisture for vegetated surface with the temporal fully polarimetric L-band SAR meaurements. Using the decomposed scattering measurements from JPL/AIRSAR image data, we evaluated their usage to reduce the vegetation effect on estimation soil moisture under configurations of a single-frequency (L-band) and multi-pass with a same incidence.
Relations the Components in Backscattering Model and the Decomposed Scattering Components
In consideration of a more general natural vegetated surface, we construct our first-order physical based scattering model as a discontinuous vegetated backscattering model. This model can be written as
Where f
v is the fraction of vegetation cover in a given pixel.
L2nn=exp(-2kappd sec(q)) is the double pass attenuation factor.
Ka is the volume extinction coefficient depending on the polarization configurations. d is the thickness of the vegetation layer and
q is the radar incidence angle.
The superscript or subscript pp represents polarization configuration for either VV or HH. The subscript t, v, s, and sv are for total, volume, surface, and surface-volume interaction terms. The direct volume scattering and the surface-volume interaction scattering terms in (2) can be written as
and
Where K
5pp is the volume scattering coefficient. R is the surface reflectivity. Both varies with the polarization parameter pp.
Since both volume extinction and scattering coefficients are strongly depended on the number density, shape and size parameters, as well as orientation or structure of the vegetation canopy, in addition to canopy thickness, it would be extremely difficult to estimate the surface scattering component or soil moisture from a limited SAR measurements.
By comparison of backscattering components in (2) and the decomposed scattering components in (1), we can approximately write
Where subscript number 1 and 2 represent the single and double reflection components.
Advantage of (2) is that it represents a general case of natural surface. It can be applied to both the fully vegetated surface (continuous layered vegetation when
f
v = 1) and the bare surface (when f
v = 0).
