Multilayer soil model for improvement of soil moisture estimation using the small perturbation method

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1 Journal of Applied Remote Sensing, Vol. 3, 3367 (4 December 9) Multilayer soil model for improvement of soil moisture estimation using the small perturbation method Kaijun Song, a,b Xiaobing Zhou, a and Yong Fan b a Montana Tech of the University of Montana, Department of Geophysical Engineering, Butte, MT 971 kaijun.song@hotmail.com, xzhou@mtech.edu b University of Electronic Science and Technology of China, EHF Key Lab of Fundamental Science, School of Electronic Engineering, Chengdu, 614, China kjsong@ee.uestc.edu.cn Abstract. A multilayer soil model is presented for improved estimation of soil moisture content using the first-order small perturbation method (SPM) applied to measurements of radar backscattering coefficient. The total reflection coefficient of the natural bare soil including volume scattering contribution is obtained using the multilayer model. The surface reflection terms in SPM model are replaced by the total reflection coefficient from the multilayer soil surface in estimating soil moisture. The difference between the modified SPM model and the original SPM surface model is that the modified SPM model includes both the surface scattering and the volumetric scattering of the natural bare soil. Both the modified SPM model and the original SPM model are tested in soil moisture retrievals using experimental microwave backscattering coefficient data in the literature. Results show that the mean square errors between the measured data and the values estimated by the modified SPM model from all samples are.%, while errors from the original SPM model are 8.4%. This indicates that the capability of estimating soil moisture by the SPM model is improved when the surface reflection terms are replaced by the total reflection coefficients of multilayer soil model over bare or very sparsely vegetation covered fields. Keywords: Soil moisture, multilayer model, backscattering coefficient, reflection coefficient, modified small perturbation model. 1 INTRODUCTION Soil moisture content is a key parameter in understanding land atmosphere interactions [1-3]. Availability of data regarding spatial and temporal variability of soil moisture over large regions would likely enhance the accuracy of numerical weather prediction (NWP), hydrological flood forecasting, agricultural drought monitoring, as well as water cycle research related to climate studies [4-8]. On one hand, sporadic in situ observations based on standard instrumentation can only measure soil moisture content at limited spatial extent and may not sample heterogeneous land surfaces at adequate resolution. On the other hand, dense ground sensor networks for measuring soil moisture content are expensive to install and maintain. In recent years, many research efforts have focused on the development of remote sensing techniques to map soil moisture content at a large scale and at adequate resolution [9-11]. These techniques include optical, infrared, passive microwave, and active microwave remote sensing. The temporal resolution of optical remote sensing and infrared remote sensing techniques has been limited by the weather and time. However, the microwave remote sensing, including the passive and active microwave remote sensing, has the capabilities of all-weather and night-and-day measurement. Compared with the passive microwave remote 9 Society of Photo-Optical Instrumentation Engineers [DOI: / ] Received 4 Feb 9; accepted 4 Nov 9; published 4 Dec 9 [CCC: /9/$.] Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 1

2 sensing, the active microwave remote sensing has much higher spatial resolution. In theory, the soil dielectric constant increases with increasing soil water content. These variations can be detected effectively by remote microwave sensors [1], and the soil moisture content can be retrieved from the measurements using an inversion model. The inversion model development and validation are thus essential before the general application of microwave remote sensing to map soil moisture. Some methods have been developed to estimate the soil moisture profile using remotely sensed soil moisture data at the surface [13-17]. But the retrieval of soil moisture profile using these methods needs not only remote sensing data but also knowledge of vertical soil moisture distribution, which is usually difficult to obtain. To circumvent the difficulties in applying theoretical models to measured radar data from natural surfaces to retrieve soil moisture, many empirical and semi-empirical models have been developed to infer soil moisture from radar measurement [18-]. It is well known, however, that empirical and semi-empirical models are site dependent and might be difficult to apply to data sets obtained under conditions beyond those under which the model was developed. Theoretical models like the small perturbation model (SPM), the physical optics model (PO) and the geometrical optics model (GO) [1] have been developed to predict the trend of radar backscattering in response to changes in soil moisture. However, they are only surface scattering inversion models, and the effect of volumetric scattering is not included. Therefore, they do not model exactly the natural soil backscattering characteristics. In surface scattering models, soil is treated as a continuous dispersive medium. The scattering process of electromagnetic wave at the air-soil interface is treated as specular reflection. However, soil which consists of various mineral particles, liquid water, and air pores, is a porous yet dense dispersive medium. This means that microwaves can penetrate into the soil and the scattering process can occur at any air-soil particle interface. Some microwave signal emerges from the soil surface after multiple scatterings, resulting in volumetric contribution in the backscattering coefficient. The objective of this paper is to develop a simple multilayer soil model that takes into account the volumetric scattering to improve estimation of soil moisture from radar backscattering coefficient data based a SPM surface model. The multilayer soil model is presented and analyzed for modeling the surface and volumetric backscattering of the natural bare soil. Based on the multilayer soil model, the total reflection coefficients for horizontally and vertically co-polarizations are obtained. These total reflection coefficients, which include volumetric scattering effect, are used to compute the backscattering coefficients, forming a modified SPM model. Soil moisture contents retrieved from microwave backscattering coefficients using the modified and the original SPM models are compared with the in situ measurements over the very sparse-vegetation fields. We find that the results are improved using the modified SPM model with volume scattering being considered, even though the volume scattering within soil is simplified as internal reflections between layers in the soil multilayer model. The proposed multilayer soil model can deal explicitly with the volume scattering effects that are not addressed in the original SPM models. The model does not need extra auxiliary information, and may be used in existing or upcoming satellite missions (e.g., SMAP and SMOS). MULTILAYER SOIL MODEL FOR SURFACE REFLECTION OF RADAR The natural soil can be viewed as a dense non-tenuous media with multiple species of particles (see Fig. 1a) []. Here we simplify the soil as composed of discrete dielectric particles of rock and water with mixing dielectric constant ε s embedded in air. Considering the difficulty and complexity in specifying the soil particles of different size, shape, and petrologic origin, radiative transfer modeling of electromagnetic waves in soil is often too complicated to obtain satisfying results. To avoid complicated radiative transfer so that the Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page

3 volume scattering in the soil can be considered, we hypothesize that the soil (z<) can be modeled as a three-layer medium, as shown in Fig. 1b with simplifications as follows: 1) The mixture of soil particles and the soil moisture is uniform along the horizontal and vertical directions, and the distribution of the soil equivalent complex dielectric constant is also uniform; ) Only single reflections are considered at the interface between layers, ignoring multiple reflections between different interfaces; thus, the volumetric scattering of soil is modeled as the sum of surface scattering terms from their interfaces between layer 1 and layer, and between layer and layer 3( ER + ER3). E R1 is the surface scattering term considered in the original first-order SPM model (see Fig.1b). Air Soil εr D (a) Ei ER1 ER ER3 air Layer 1 εs Layer ε Layer 3 ET1 Er ET Er1 ET3 Er3 ET ET4 d d1 D εr (b) Fig. 1. Multilayer medium reflection model of the soil (a) the natural soil with multiple species of particles; (b) the simple equivalent model. The test of the above hypothesis will be prediction capability of the SPM model, with or without consideration of the volume scattering. Improvement of soil moisture prediction by the SPM model with volume scattering derived from the multilayer soil model described above will indicate the hypothesis is somewhat reasonable. As shown in Fig. 1a and 1b, D is the radar penetration depth, which is approximated as [1] Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 3

4 λ D = d + d1 = (1) 4π Im[ ε ] where λ is the wavelength in free space, and ε = ε r ε = ε ' + jε '' is the relative complex dielectric constant of the natural soil. The multilayer soil model for radar reflection includes three uniform media (three layers): layer 1 with thickness d and permittivity ε s, representing the mixture of soil particles and liquid water content; layer with thickness d 1 and permittivity ε, representing the air in soil; and layer 3 with any thickness and permittivity ε r, representing the natural soil layer that is below the radar penetration depth. It has been noted that the permittivity ε s of layer 1, which represents the mixture of soil particles and liquid water content, is different from the permittivity ε r in layer 3 that is for the natural soil mixture of soil particles, liquid water content, and air porosity. The mixture of soil particles and liquid water content can be considered to be embedded uniformly in air for the natural soil. So, the permittivity ε s of the mixture of soil particles and liquid water content can be obtained from the permittivity ε r of the natural soil. If the soil porosity is denoted as φ, since the total volume of the natural soil is equal to the sum of the voids and the mixture of soil particles and liquid water, the permittivity ε s can be expressed approximately as ε r φ ε s = () 1 φ The thickness d of layer 1 is a key parameter. In combination with the attenuation factor in layer 1 and path length, ε s determines the total attenuation along a single path from one interface to the next interface in its propagation. Using the same analysis above, the thickness d of layer 1 can be given as ( ) ( d + d 1 )( 1 φ) = D( φ) d = 1 (3) d + d 1 represents the penetration depth of radar in the soil. According to the electromagnetic (EM) theory, the incidence and reflective EM waves at the interface between and layer 1 can be written as (see Fig. 1(b)) jk1 z z E = E e (4) i E 1 jk z z R1 = RaEe () E R = T1Er = T1 AEr1 = T1 ARsET = T1 ARs AET1 = T1 ARs AT1Ei (6) ER3 = T1ET = T1AET 4 = T1AT1Er3 = T1AT1RasET 3 = T1AT1RasT1ET = T1 AT1RasT1AET 1 = T1AT1RasT1AT1Ei (7) where k1 z = k 1 cosθ, θ and k 1 are the incidence angle and wave number of the incidence wave, respectively; R a is the specular reflection coefficient of the interface between air (open space) and layer 1 (soil particles and liquid water); R s is the surface reflection coefficient of the interface between layer 1 (soil particles and liquid water) and layer (air in soil); Ras is the specular reflection coefficient of interface between layer (air in soil) and layer 3 (soil). Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 4

5 R as is different from R a because of the different dielectric constant between layer 1 (ε s ) and layer 3 (ε r ). T mn is the transmission coefficient from layer m to layer n, and m, n =, 1,, 3; A is the amplitude attenuation factor [3] due to absorption, which can be given as A K e d / cosθt = e (8) where Ke is the extinction coefficient of layer 1 and θt is the refraction angle at the interface between air (open space) and layer 1 when the incident EM wave is from air to layer 1. The attenuation of EM waves in air is ignored. Since medium is air (porosity of soil), we have Rs = Ra, T 1 = T1, T 1 = T1. A is a function of the thickness d of layer 1. Since the attenuation of EM waves in air is ignored, the reflection coefficient and transmission coefficient are thus independent of the thickness d 1 of layer. From Eqs. (4)-(7), the total surface reflection coefficient from the multilayer soil becomes ~ R ( ) a = Ra + T1T1 A Ra + RasT1T1 (9) where T 1 = 1+ R1 = 1+ Ra, T1 = 1+ R1 = 1 Ra. From Eq. (9), we can see that the total surface reflection coefficient from multilayer soil model includes two terms: 1) the direct surface reflection term R a, which corresponds to the surface scattering of the soil; ) the equivalent volumetric scattering term T1T1 A ( Ra + RasT1T1), which is represented by the internal reflections between layers. We can also see from the above equations that the total reflection coefficient of the multilayer soil is independent of the thickness d 1 of layer. 3 MODIFIED SPM MODEL AND APPLICATIONS The multilayer soil model for radar reflection developed above is applied to the SPM model to test if the multilayer soil moisture model can improve the predictability of soil moisture of the SPM model. The vertical and horizontal backscattering coefficients from the SPM model are given by [1, 3-] sin θ (1 + R 4 ) 1 σvv = 8k σ1 R cos θ + 1 W(ksin θ,) ε r σ hh 1 4 = 8k σ R cos θ W (k sinθ,) (11) where k is the wave number of free space, σ1 the surface Root Mean Square (RMS) height, θ the incidence angle, ε r the soil dielectric constant, R and R the vertically and horizontally polarized Fresnel reflection coefficients, respectively, and W ( k sinθ,) the Fourier transform of a known surface correlation function which can be calculated by (1) W u) = ρ x exp( jxu) dx (1) ( ( ) where ρ is the surface correlation function. The SPM model is only a surface scattering model. However, when the total reflection coefficient from the multilayer soil surface is used to replace the surface reflection in the SPM Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page

6 model, we obtain a modified SPM model, which includes both surface scattering from air-soil interface and a volumetric scattering term within the soil. Both the original and modified SPM models are used to calculate radar backscattering coefficient and the results are compared with in situ measurements [, ]. For the data set from Fung et al. (199) [], comparisons are made for the case in which the soil surface RMS height is.4 cm, the correlation length is 8.4 cm, the frequency is 1.1 GHz, the soil porosity is %, the incident angle range is from 1 to 7, and the surface moisture content is 14% in volume. The dielectric constants are derived from the following relation [6, 7] M v ε r.ε r +.43ε r = (13) For Oh et al. s 199 data set [], the soil surface RMS height is.4 cm and.3 cm, the correlation length is 8.4 cm and 9.9 cm, respectively. The frequency is 1. GHz, and the incidence angle is from 1 to 7. An exponential correlation function was found to fit the two surface profiles. 1 Backscattering Coefficients(dB) SPM Modified SPM Measured data Incident Angle (deg) 1 (a) Backscattering Coefficients(dB) SPM Modified SPM Measured data Incident Angle (deg) (b) Fig.. Comparison of backscattering simulations with measured data obtained from Fung et al., 199 [17]. (a) VV polarization; (b) HH polarization. Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 6

7 Backscattering Coefficients(dB) SPM Modified SPM Measured data f=1. GHz L=8.4 cm σ=.4 cm Mv=1% VV polarization Incident Angle (deg) Backscattering Coefficients(dB) f=1. GHz L=9.9 cm σ=.3 cm Mv=1% VV polarization - SPM -3 Modified SPM Measured data Incident Angle (deg) Backscattering Coefficients(dB) (a) f=1. GHz L=9.9 cm σ=.3 cm Mv=14% VV polarization - SPM -3 Modified SPM Measured data Incident Angle (deg) Backscattering Coefficients(dB) (b) f=1. GHz L=9.9 cm σ=.3 cm Mv=14% HH polarization -4 SPM Modified SPM Measured data Incident Angle (deg) (c) (d) Fig. 3. Comparison of backscattering simulations with measured data obtained from Oh et al., 199 []. Figures and 3 show the comparison of backscattering coefficients calculated by the modified SPM model and the original SPM surface model with the measured data from Fung et al. (199) [] and Oh et al. (199) [], respectively. It can be seen that compared with the original SPM model, the backscattering coefficients calculated by the modified SPM model increase with inclusion of the volumetric scattering for the incidence angle up to 7 for the HH co-polarization. The difference in backscattering coefficients between the modified and original SPM models decreases with increasing incidence angle. For VV polarization, the backscattering coefficients calculated by the modified SPM model is almost equal to that of original SPM model at the incidence angle of about 6 in Fig. (a). However, the backscattering coefficients calculated by the modified SPM model are less than that of original SPM model when the incidence angle is large about 6 in Fig. (a). This may indicate that the modified SPM model is valid when the incidence angle is less than about 6 in this case. The modified SPM model should be used with caution for microwave remote sensing at large incidence angles (especially at incidence angle larger than 6 ) for VV copolarization. Nevertheless, the backscattering coefficient calculated with the modified SPM model agrees better with the measurements than the original SPM model. Improvement in predicting the radar backscattering coefficient by the modified SPM model can be clearly seen in Fig. and Fig. 3. Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 7

8 To evaluate the improvement in soil moisture content retrievals using the modified SPM model, an inversion technique using the genetic algorithm (GA) for retrieving soil moisture from multi-polarization radar observations of bare natural soil has been developed [8, 9]. The procedure of retrieval of soil moisture contents from measured backscattering coefficients is shown in Fig. 4. Firstly, the iterative procedure produces random estimated values of the soil moisture content for all measured data points. Secondly, the estimated dielectric constants are derived from the random estimated values of the soil moisture content using Eq. (13). Thirdly, the backscattering coefficients estimated by the modified SPM model are compared with the in situ measurements. Fourthly, the errors between the backscattering coefficients estimated by the modified SPM model and the measurements are evaluated. If the desired errors are obtained, then the iterative procedure is finished, and the desired soil moisture contents can be obtained; otherwise, the new values of soil moisture contents can be generated by natural selection, clone, crossover, and mutation. Fifthly, the procedure returns to the first step and repeats the above procedure. Finally, the desired values of the soil moisture contents are obtained. The accuracy of the estimation is then assessed with a correlation analysis between the measured and the estimated values of the soil moisture. In the above procedure, the errors of backscattering coefficients between the modified SPM model and the measurements is a cost function for the GA, which is defined as c pp σ pp ( ε r ) σ m = (14) where σ pp and σ m are the backscattering coefficient of the modified SPM model and the measurements, respectively. c pp is the horizontally ( p = h ) or vertically ( p = v ) copolarized cost function. The inversion procedure has been applied to the measured radar backscatter data as described in Bolten et al., 3 [3] and Sano et al [31]. For the measured data set described in Bolten et al. (3), the Passive and Active L- and S-band airborne sensor (PALS) [3, 3] was developed to study the utilization of dual-frequency, dual-polarization, passive, and active measurements for remote sensing of ocean salinity and soil moisture. The instrument operates at 1.4GHz and.69 GHz in the radiometer channels and 1.6GHz and 3.1 GHz in the radar channels. PALS utilizes a multi-frequency multi-polarization design and is capable of acquiring simultaneous radar and radiometric signatures of land and ocean surfaces. The instrument provided simultaneous collection of horizontally and vertically polarized L- and S-band brightness temperature and backscatter coefficients. The unique active/passive design provides valuable information on the corresponding effects of varying vegetation, surface types and soil moisture on the radar and radiometer responses. The radar transmits vertical or horizontal polarization and receives these two linearly polarized radar echoes simultaneously. Here, only the L-band backscattering coefficients of vertical (LVV) and horizontal (LHH) polarization are used. All of the samples are chosen under the condition of the applicable roughness parameters of the SPM model. The RMS height is. cm, the correlation length is chosen to be cm, and the frequency is 1.6 GHz. The incidence angle of radar sensor is 39, and the soil moisture content is from 6% to 4%, corresponding to dielectric constant from 4 to 13. Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 8

9 Generate Random Soil Moisture Contents Estimate Dielectric Contants Using (13) Compute Backscattering Coefficients Using Modified SPM Model Comparison of Backscattering Coefficients computed by Modified SPM Model with Measurements Desired Error is Obtained? Yes Finish No Natural Selection of Soil Moisture Content According to Fitness Clone of Soil Moisture Content Crossover Mutation Fig. 4. Procedure of Retrieval of Soil moisture contents from measured backscattering coefficients. The data from Sano et al are provided by the Sandia National Laboratories (SNL) in Albuquerque, New Mexico. The RMS height is from. to. cm, the correlation length is cm and 6 cm, and the frequency is.3 GHz. The incidence angle is 3, and the measured soil moisture content varies from 1% to 36%, corresponding to dielectric constant from 6 to 1. All of the results are at VV co-polarization. Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 9

10 Inferred in % 3 Inferred in % VV-Bolten et al., 3 HH-Bolten et al., 3 VV-Sano et al., : Measured in % (a) 1 1 VV-Bolten et al., 3 HH-Bolten et al., 3 VV-Sano et al., : Measured in % (b) Moisture error between original and modified IEM % VV-Bolten et al., 3 HH- Bolten et al., 3 VV-Sano et al., Measured in % (c) Fig.. Comparison of the soil moisture content measured with the estimated by (a) the original SPM surface model and (b) the modified SPM model. (c) Shows the estimated soil moisture error between the original and modified SPM. VV(HH)- Bolten et al., 3 stands for the measured soil moisture data of LVV (LHH) described in Bolten et al. (3). VV-Sano et al., 1998 is for the measured soil moisture data of LVV described in Sano et al. (1998). The results are illustrated in Fig.. Figure (a) shows the comparison of the soil moisture content retrieved by the original SPM surface model and the measured values, while Fig. (b) shows the comparison of the soil moisture content retrieved by the modified SPM model and the measured values. Results show that the mean square errors between the measured data and the values estimated by the modified SPM model from all samples are.%, while those between the measured and the estimated by the original SPM model are 8.4%. This indicates that the capability of estimating soil moisture by the SPM model is improved by 3.% when the surface reflection in the original SPM model are replaced by the total surface reflection coefficients calculated by the multilayer soil model. Also, these results indicate a good capability in retrieving soil moisture values for L-band and C-band. The estimated soil moisture errors between the original and modified SPM are shown in Fig. (c), which shows the soil volumetric scattering contribution in the soil moisture retrieval. The contribution of the volumetric scattering is about 1-18% from Fig. (c). But we can also see from Fig. (c) that most of the contribution of the volumetric scattering is less than 1%. The overestimation Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 1

11 may be caused by parameter errors. The mean square errors from all samples shown in Fig. (b) are also.%. The measured soil moisture content and the backscattering coefficients from the PALS data [3] are collected over the low-vegetated fields, and the components of scatter from vegetation have been ignored, which can introduce errors between the measured soil moisture contents and those inferred by the proposed model. In addition, the radar system noise and the inaccurate surface autocorrelation function may also contribute to errors in the retrieval of the soil moisture content from the radar measurement data. 4 CONCLUSIONS A multilayer soil model for radar reflection is developed. Based on the model, the total reflection coefficient has been obtained, which includes both the surface reflection term that corresponds to the direct surface scattering of the natural soil, and the volumetric reflection term, which is simplified as the single reflections from the internal interfaces of the multilayer model. The total reflection coefficient including volumetric scattering is used to replace the surface reflection in the SPM model to obtain a modified SPM model. Comparisons of backscattering coefficient simulations using the modified SPM model show good agreement with the experimental data obtained from the literature, and the modified SPM model is also compared with the original SPM surface model. Estimated soil moisture from the radar observation data by the modified SPM model gives comparable results to the measured data [, 3] over the very sparse-vegetation fields. The mean square errors from all samples are.% for the modified SPM model, comparing to 8.4% for the original SPM model. The results indicate that the predicting capability of SPM model is improved with volumetric scattering being considered with the multilayer soil model for radar reflection over bare or very sparsely vegetation covered fields. For the partially vegetated fields, this multilayer soil model can be further modified by adding a top layer of vegetation [33] to simulate the impact of the vegetation.. Acknowledgments The research was supported by NASA through Montana Space Grant Consortium (G W381), by National Natural Science Foundation of China-NASF (Grant No: 1976), and by Youth Science & Technology Foundation of the University of Electronic Science and Technology of China (JX711). References [1] T. Delworth and S. Manabe, "The influence of soil wetness on near surface atmospheric variability," J. Climate, (1989) [doi: 1.117/1-44(1989)<1447:TIOSWO>..CO;]. [] K. L. Brubaker and D. Entekhabi, "Analysis of feedback mechanisms in landatmosphere interaction," Water Resources Res. 3, (1996) [doi:1.19/96wr]. [3] M. J. Fennessy and J. Shukla, "Impact of initial soil wetness on seasonal atmospheric prediction," J. Clim. 1(11), (1999) [doi: 1.117/1-44(1999)1<3167:IOISWO>..CO;]. [4] R. A. Pielke, Sr., "Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall," Rev. Geophys. 39- (1). [] P. A. Dirmeyer, F. J. Zeng, A. Ducharne, J. C. Morrill, and R. D. Koster, "The sensitivity of surface fluxes to soil water content in three land surface schemes," J. Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 11

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13 [] L. Tsang, "Dense media radiative transfer theory for dense discrete random media with particles of multiple sizes and permittivities," Progress in Electromagnetics Research, vol. 6, Dielectric Properties of Heterogeneous Materials, Chap., pp , A. Priou, Ed., Elsevier, New York (199). [3] F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing, Active and Passive, vol.1, Addison-Wesley, Norwood, MA (1981). [4] A. K. Fung, Microwave Scattering and Emission Models and Their Applications, Artech House, Norwood, MA (1994). [] A. K. Fung, Z. Li, and K. S. Chen, "Backscattering from a randomly rough dielectric surface," IEEE Trans. Geosci. Rem. Sens. 3, (199) [doi: 1.119/ ]. [6] G. C. Topp and J. L. Davis, "Measurements of soil water content using time domain reflectometry: a field evaluation," Soil Sci. Am. J. 49, 19 4 (198). [7] G. C. Topp, J. L. Davis, and A. P. Annan, "Electromagnetic determination of soil water content: Measurement in coaxial transmission lines," Water Resources Res. 16, 74-8 (198) [doi:1.19/wr16i3p74]. [8] Y. Oh, "Robust inversion technique for retrieving soil moisture from multi-polarised backscatter of bare surface," Electron. Lett. 4, (6) [doi: 1.149/el:6483]. [9] Y.-Q. Wang and Y.-Q. Jin, "A genetic algorithm to simultaneously retrieve land surface roughness and soil moisture," J. Rem. Sens. 4, 9-94 (). [3] J. D. Bolten, V. Lakshmi, and E.G. Njoku, "Soil moisture retrieval using the passive/active L- and S-band radar/radiometer," IEEE Trans. Geosci. Rem. Sens. 41, (3) [doi: 1.119/TGRS ]. [31] E. E. Sano, M. S. Moran, A. R. Huete, and T. Miura, "C- and multiangle Ku-band synthetic aperture radar data for bare soil moisture estimation in agricultural areas," Rem. Sens. Environ. 64, 77-9 (1998) [doi:1.116/s34-47(97)17-3]. [3] J. W. Wilson, S. H. Yueh, S. J. Dinardo, S. L. Chazanoff, A. Kitiyakara, F. K. Li, and Y. Rahmat-Samii, "Passive active L- and S-band (PALS) microwave sensor for ocean salinity and soil moisture measurements," IEEE Trans. Geosci. Rem. Sens. 39, (1) [doi: 1.119/36.914]. [33] M. C. Dobson and F. T. Ulaby, "Preliminary evaluation of the SIR-B response to soil moisture, surface roughness, and crop canopy cover," IEEE Trans. Geosci. Rem. Sens. GE-4, 17 6 (1986) [doi:1.119/tgrs ]. Kaijun Song received the M.S. degree in radio physics and the Ph.D. degree in electromagnetic field and microwave technology from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in and 7, respectively. He has been with the EHF Key Lab of Fundamental Science, School of Electronic Engineering, UESTC, since 7, where he is currently an Associate Professor. From August 7 to July 8, he was a postdoctoral research fellow with the Montana Tech of the University of Montana, Butte, USA, on microwave remote sensing technology and microwave circuits. He is currently a research fellow with the State Key Lab of Millimeter Waves, Department of Electronic Engineering, City University of Hong Kong, on Electromagnetic theory and the design of MMIC and THz technology. He is an IEEE member. His current research fields include microwave remote sensing theory, modeling, and algorithm; applications in geoscience, geophysics, geology, hydrology, and environmental sciences; electromagnetic theory; and microwave/millimeter-wave devices, circuits, and systems. Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 13

14 Xiaobing Zhou received the B.S. degree in physics from Hunan Normal University in 1986, the M.S. degree in theoretical physics from Sichuan University in 1989, and the Ph.D. degree in geophysics, with specialization in remote sensing, from the University of Alaska, Fairbanks, in. He was with the Southwestern Institute of Physics, Chengdu, China as a research assistant professor in 1989 and a research associate professor in 199; with the University of California, San Diego, as a visiting scientist in 1997; with the University of Alaska, as a visiting scientist in 1998; with New Mexico Tech, Socorro, as a research assistant professor in hydrology from to ; and with the Department of Geophysical Engineering, Montana Tech of the University of Montana, Butte, USA as an assistant professor in geophysics from to 9. He is now an associate professor with Montana Tech of the University of Montana. His current research interests include remote sensing theories, algorithm development, and applications in earth and environmental sciences and applied geophysics. Yong Fan received the B.E. degree from Nanjing University of Science and Technology, Nanjing, China, in 198 and the M.S. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 199. He is currently with the School of Electronic Engineering, University of Electronic Science and Technology of China. From 198 to 1989, he was interested in microwave integrated circuits. He has authored or coauthored over 9 papers, 3 of which are searched by SCI and EI. Since 1989, his research interests have been including millimeter-wave communication, electromagnetic theory, millimeter-wave technology, and millimeter-wave systems. Prof. Yong Fan has been an IEEE member from 6 to now, and he is also a senior member of the Chinese Institute of Electronics. Journal of Applied Remote Sensing, Vol. 3, 3367 (9) Page 14