Using an EM Model in Remote Sensing of Soil Surface from Polarimetric SAR

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Using an EM Model in Remote Sensing of Soil Surface from Polarimetric SAR

Inversion results and discussions
The test area was located at Little Washita river watershed, southern of Chickasha, Okhaloma. The area covering approximately 610 square kilometers, is wet and damp in its weather with annual rain rate about 750 mm mostly fallen in spring and fall season. The terrain in mild sloping, the height relief is no more than 150 meters, except a stone mountainous at the north-east region; therefore, it is very suitable for testing the surface parameters inversion from SAR observations. SAR data set was acquire by a L-band (f=1.254 GHz)SAR system during NASA SIR-C/X-SAR mission in 1994. it was a multi-temporal campaign consistent with ground based scatterometer. During the mission , extensive ground truth collection was conducted. Table 1 list some important parameters in certain area.

The inversion results using different models to train the neural network were given in Table 2. in comparison with limited available ground truth data, it was found that the regular IEM can invert most of the data points, but all are wrong results, despite of its combined with TD. We observe that the inversion results using the dielectric profile agree well with the ground truth. This is particularly true where the TD was combined. For the moment, we may conclude that the dielectric profile simulates very closely the practical situation of the soil moisture content. It is noted that the relationship between the dielectric constant and moisture content can be found in [Hallikainen et al., 1985]. When such profile was adopted, the modified Fresnel reflection coefficient were able to obtain and subsequently cast into the regular IEM model. The only parameter remain to select is the dielectric change rate. Further determination of it should be carried out experimentally.

			Vegtation biomass samples
Test site	cover	date	Averge(g/cc)	Water Content (g/m²)	Height Std. Dev.(cm)	Correl.Length (cm)
11	Alfalfa	40694	1.36	1798	0.81	11.26
12	Baresoil	40594	1.05		3.43	13.03
13	Winter wheat	40694	1.42	1386	0.81	6.05
13	Winter wheat			2104
14	Range	40694	1.01	96	0.73	8.77
15	Spring oaks	41494	1.20
21	Range	40494	1.14	78	0.87	10.62
22	Range	40994	1.24	107	0.67	10.98
23	Range	40794	1.36	65	1.31	12.89

Table 1: Ground truth samples (from T.J.Jackson, 1992)

Site	Correlation Length(cm)	Height stdDev.(cm)	Dielectric constant(real part, imagery part)
11	8.081	1.569	17.743	1.760
12	8.210	1.451	16.091	1.620
13	7.944	1.523	15.373	1.554
14	7.841	1.512	17.731	1.754
15	9.631	1.443	19.026	1.869
21	7.323	1.405	15.174	1.524
22	8.972	1.443	18.899	1.851
23	7.730	1.523	17.880	1.760

Table 2 : TEM (Dielectric profile)+TD
Conclusion
A soil scattering model based on the IEM model was proposed in an effort to better estimation of soil surface parameters- primary roughness scales and moisture content .This was done by simply replacing the standard reflection coefficients by a layer medium reflection coefficient under the framework of IEM . Model simulation were done to illustrate the difference. An eigenvector - based target decomposition theorem was applied to SAR imagery data to extract those direct soil surface scattering terms .The resulting data were then fed in to a neural net work trained by the soil scattering model to start the parameter in versions. Resulting using the SIR-C data show that the proposed model gives the best etimate among the method . Conclusion can be made that the proposed model can explain more closely the observed data and hence give the best inversion result. The only free parameter left is the change rate, m which need to be further studied.

Reference :

A.K. Fung, Z. Li, and K.S. Chen. "Backscattering from a randomly rough dielectric surface, "IEEE Trans. Geosci. Remote Sensing, vol. 30, pp. 356-369, 1992.
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L.M. Brekhovshikh, Waves in Layered Media, ACADEMIC PRE-SS, 1980. M.T. Hallikainen, F.T. Ulaby, M.C. Dobson, M.A. El-rayes, and Lin-Kun Wu, "Micro-wave Dielectric Behavior of Wet Soil -Part 1 : Empirical Models and Experimnetal observations", IEEE Trans. Geosci, Remote Sensing, vol GE-23, NO. 1, PP. 25-34, Jan. 1985.
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Y.C. Tzeng, K.S. Chen, W.L. Kaso, and A.K. Fung, "A Dynamic Learning Neural Network for Remote Sensing Applications "IEEE Trans. Geosci. Remote Sensing, vol. 32, no. 5, pp. 1096-1101, Sep. 1994.
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