Using an EM Model in Remote Sensing of Soil Surface from Polarimetric SAR
Retrieval Scheme
The use of any retrieval algorithm s a good set of measured data upon which to validate the accuracy signal model can be in one of many forms including empirical algorithms or a closed -form model such as the IEM. To date , a significant problem has been that there is a severe dearth of measurements with a complete set of consistent ground truth . In many existing sets encountered , the ground truth does not contain a soil moisture profile as required in our model . However , its need can be demonstrated using a set if Washita data , where measurements were acquired over a period of time after rain.
Decomposition of SAR return
As a preprocess step , we shall apply a target decomposition theorem to decompose a SAR total return signal which contains contribution from many sources - among them only return from ground are directly related to soil moisture. Therefore , it will be proven to be useful to single out the ground scattering term for purpose of retrieval. We include here the explicit expressions for completeness.
The eigenvector - based decomposition proposed by Cloude [1996] will be used in this paper. We can write the reflection symmetric convariance matrix as follows
The coherence matrix is then of the form.
The eigenvalue of coherence matrix [T] can be calculated as
| r = |
ShhSvv*
-------------
ShhShh |
(9) |
hShvShv* (10)
It is easy and straightforward to obtain (8)-(10) from muller matrix. Once the eigenvector is obtained,the total scattering return can then be decomposed.
Retrieval Procedure
The use of neural network as an inverter of remote sensing parameters has been documented [T sang et al., 1993; Tzeng et al, 1994; Chen et al, 1995; Chen et al, 1998]. Its effectiveness and usefulness has been demonstrated from a wide range of parameters acquired by different sensors. This study apply a dynamic learning neural network to perform the soil surface parameters inversion from the backscattering coefficient estimated from the SAR acquisitions from SIR-C mission.
