IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 43, NO. 12, DECEMBER 2005
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A Parameterized Multifrequency-Polarization Surface Emission Model Jiancheng Shi, Senior Member, IEEE, Lingmei Jiang, Student Member, IEEE, Lixin Zhang, Kun-Shan Chen, Senior Member, IEEE, Jean-Pierre Wigneron, and André Chanzy
Abstract—This study develops a parameterized bare surface emission model for the applications in analyses of the passive microwave satellite measurements from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). We first evaluated the capability of the advanced integral equation model (AIEM) in simulating wide-band and high-incidence surface emission signals in comparison with INRA’s field experimental data obtained in 1993. The evaluation results showed a very good agreement. With the confirmed confidence, we generated a bare surface emission database for a wide range of surface dielectric and roughness properties under AMSR-E sensor configurations using the AIEM model. Through the evaluations of the commonly used semiempirical models with both the AIEM simulated and the field experimental data, we developed a parameterized multifrequency-polarization surface emission model—the Qp model. This model relates the effects of the surface roughness on the emission signals through the roughness variable Qp at the polarization p. The Qp can be simply described as a single-surface roughness property—the ratio of the surface rms height and the correlation length. The comparison of the emissivity simulations by the Qp and AIEM models indicated that the absolute error is extremely small at the magnitude of 10 3 . The newly developed surface emission model should be very useful in modeling, improving our understanding, analyses, and predictions of the AMSR-E measurements. Index Terms—Microwave, roughness.
modeling,
surface
emissivity,
I. INTRODUCTION
T
HE surface emission or effective reflectivity model is one of the essential components in many applications of the microwave remote sensing of geophysical properties in the complex earth terrain. It is a direct component in monitoring soil moisture in the bare [1]–[6] or vegetated surfaces [7]–[11]. The multifrequency and polarization measurements are, preferably, used to derive the surface geophysical and atmospheric properties. For instance, a multifrequency-polarization iterative
Manuscript received May 9, 2005; revised July 15, 2005. This work was supported by the Chinese Special Funds for Major State Basic Research Project (G2000077908). J. Shi is with the State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, 100101 Beijing, China and also with the Institute for Computational Earth System Sciences, University of California, Santa Barbara, CA 93106 USA (e-mail:
[email protected]). L. Jiang and L. Zhang are with the Center of Remote Sensing and GIS, Beijing Normal University, 100875 Beijing, China. K.-S. Chen is with the Center for Space and Remote Sensing Research, National Central University, 32054 Chung-Li, Taiwan, R.O.C. J.-P Wigneron and A. Chanzy are with Institute National de Recherches Agronomiques (INRA), F-84914 Avignon, France. Digital Object Identifier 10.1109/TGRS.2005.857902
algorithm has been used to derive the surface soil moisture and vegetation water content [9] or a combined surface roughness and vegetation parameter [10]. The estimates are adjusted iteratively in computations of TB and TB using the measurements from 6.9–18 or 10–18 GHz (depending on radio-frequency interference condition) until the difference between computed and observed brightness temperatures is minimized in a least squares sense. Paloscia et al. [11] developed a polarization index algorithm to estimate soil moisture—this approach describes that soil moisture is linearly related to the brightness temperature measurements at 6.9 GHz and uses the normalized brightness temperature (polarization index) at 10 GHz to correct vegetation effects on the intercept and slope of the linear relationships at 6.9 GHz [11]. The surface emission or effective reflectivity model serves as the boundary condition in studying snow and atmospheric properties. It has been found that the underground surface emission signals have a great impact on snow water equivalence retrieval [12] when using the Advanced Microwave Scanning Radiometer-Earth Observing System’s (AMSR-E) brightness temperature gradient (difference between 18 and 37 GHz) algorithm [13]. There are two types of the approaches—the physical modeling and semiempirical approaches that are commonly used in modeling the surface emission or reflectivity. The semiempirical approach—this type of models are generally found to be easy to use on image based data analyses without significant computing efforts and serve as basis of the direct inversion models for ground surface properties and the boundary conditions for retrieving other geophysical parameters. The model parameters used in semiempirical approaches are often derived from limited field observations. They typically work well on the same dataset for the purpose of the application that was developed. However, it always needs to be evaluated when applying to other datasets or application purposes. This is because all errors from the measurements including the quality of the instruments, calibration error, and the characterizations of the ground surface roughness and soil properties (moisture, texture, and temperature) at the footprint scales of the instrument measurements. Furthermore, the current available empirical or semiempirical models that are suitable for the image based analyses were mainly developed by the low-frequency and low-incidence measurements. There are great sensitivity differences of the surface emission signals to the surface roughness properties at different frequencies and polarizations. A large uncertainty is expected when they are applied to the current available high-frequency and high-incidence satellite measurements such as AMSR-E. It is clear that the incorrect description of the characteristics in terms of frequency and polarization responses of surface emission signals
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will significantly affect on the accuracies of estimating geophysical and atmospheric properties. Therefore, their ability for applications to the current available satellite measurements needs to be evaluated. In theoretical models, the surface effective reflectivity— commonly consists of two components: the coherent component and noncoherent component that can be obtained by the hemispherical integrating the bistatic scattering coefficient over the upper medium [14]–[16]
(1) The subscript or describes the polarization state v or h and indicates the scattering direction. The is the Fresnel reflectivity for a flat surface. is wavenumber in free space and is the standard deviation of surface height. In recent theoretical model developments, the integral equation model (IEM) has demonstrated a much wider application range for surface roughness conditions than that from conventional models such as small perturbation model, physical optical model, and geometric optical model [16]. Recently, Chen et al. [17] extended the original IEM and developed the advanced integral equation model (AIEM). The improvements were mainly done by removing some weak assumptions in the original IEM model development. They include the following. 1) The complementary field coefficients were rederived to keep the absolute phase terms in the surface Green’s function and its gradient, leading to more complete, and thus more accurate, expressions of the multiple scattering [18] and single-scattering terms [17]. 2) In calculation of the bistatic scattering coefficient of randomly dielectric rough surfaces, the Fresnel reflection coefficients are usually assumed to be either at the incident angle or the specula angle in the low- and high-frequency range respectively. However, these two considerations are only applicable to their respective regions of validity. A physical-based transition function that naturally connects these two approximations was proposed by [19] and was included in the new version of the AIEM model [17]. The comparisons of AIEM with a three-dimensional (3-D) Monte Carlo model simulated data [17] showed a significant improvement in prediction accuracy over the original IEM model. It allows more accurate calculation of surface emission signals over a wide range of surface dielectric, roughness, and sensor frequencies. This improvement has made possible to simulate surface emission signals for the very rough surface conditions or for the high frequencies. However, the tests of the theoretical AIEM model were only performed with the surface roughness parameters of the rms height and correlation length less than 0.41 and 1.33 of the wavelength [17] that is suitable for the applications with the low-frequency (L-band) sensors. At high frequencies (C-band or higher), the natural surface roughness conditions can be much larger than the above tested roughness scales. Furthermore, the complexity of this model makes
its direct application for analyses of microwave radiometer data or inferring geophysical parameters rather difficult in the sense of computation although the AIEM model is valid for a wider range of surface roughness conditions when compared to other early theoretical models. Therefore, there is a need to develop a simple and accurate surface emission model that can correctly present the characteristics and relationships between the surface emission signals from the measurements of the multifrequency and polarization sensors and that can be easily used in the passive microwave remote sensing applications. The passive microwave satellite measurements are available from AMSR-E on Aqua (formerly EOS-PM). They provide the measurements with the large incidence angle of 55 and with the multifrequency and polarization for monitoring the geophysical properties of the land, ocean surfaces, and atmosphere. The objectives of this study are: 1) to evaluate the AIEM model for its ability in the applications to the current available multifrequency and high-incidence sensor AMSR-E and 2) to develop a parameterized surface emission model using the database simulated by the AIEM model for a wide range of surface roughness and soil moisture conditions under the AMSR-E sensor configurations. Due to our focus of this study is to develop a parameterized surface emission model for microwave remote sensing of the surface geophysical applications such as soil moisture, vegetation and snow properties, the sensor parameters of AMSR-E for the frequencies considered in this study are 6.925, 10.65, 18.7, 23.8, and 36.5 GHz from C-band to Ka-band. We first show the evaluation of AIEM model by comparison with the INRA field experimental data obtained in 1993 in next section. With the confirmed its capability in simulation of the high-frequency and high-incidence surface emission signals, we generated a simulated database using AIEM model with a wide range of the surface roughness and soil moisture conditions. The simulated data along with the experimental data will be used to demonstrate the basic characteristics of the surface roughness effects on the emission signals at different frequencies polarizations as shown in Section III. In Section IV, we show the evaluation of the commonly used semiempirical model forms and a newly proposed form by this study using both the AIEM simulated and the field experimental data. We then demonstrate how the parameters in the newly developed form can be simply related to the measurements of the surface roughness properties in Section V, and followed by our conclusions from this study. II. COMPARISON OF AIEM MODEL WITH THE FIELD EXPERIMENTAL DATA For the objective of this study, the field experimental dataset used in this study was obtained from a crane-mounted multifrequency microwave radiometer PORTOS at the frequencies of 5.05, 10.65, 23.8, and 36.5 GHz at the incidence of 50 over 18 40 m bare fields in 1993 at the remote sensing test site of the Institute National de Recherches Agronomiques (INRA), Avignon, France. These measurements are used to evaluate the capability of AIEM in the application to the high-frequency and high-incidence data and to demonstrate the important surface emission characteristics. The brightness temperature measurements had the footprint size about 15 m 25 m. This dataset was acquired during April 20 to July 10, 1993 over several bare fields with a very large range of the surface roughness conditions
SHI et al.: PARAMETERIZED MULTIFREQUENCY-POLARIZATION SURFACE EMISSION MODEL
from a rough freshly plowed field to a very smooth surface. A large range of the soil moisture conditions was obtained by irrigating the fields and then letting them dry out during the experiment. The detailed information for the soil moisture and surface roughness measurements, their parameters, and the techniques used to derive these parameters can be found in [5]. To derive the surface emission signals from the PORTOS radiometer measurements, we performed the following preprocess on the radiometer and surface property measurements. When there were several radiometer brightness temperature measurements TB at each frequency and polarization from PORTOS instrument but only one soil moisture value was prowas calculated by the average vided, the surface emissivity value for the same frequency and polarization to represent the observations from the instrument TB
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TABLE I SUMMARY OF THE VOLUMETRIC SOIL MOISTURE AND THE SURFACE ROUGHNESS PARAMETERS MEASURED IN EACH FIELD SITE
(2)
were is the downward sky brightness temperature. It is estimated by the technique given by Calvet et al. [20]. is the is the surface temperature that was number of observations. obtained by averaging from the surface temperature profile measurements at the surface, 0.5- and 1-cm soil depth. For AIEM model input parameter—dielectric constant for each field site was calculated by a semiempirical model [21]. We used the corresponding soil bulk density, texture, and the average soil moisture from several (generally around 5 at each site and time of measurement) vertical soil moisture profile measurements at the surface, 0.5- and 1-cm soil depth since the penetration at high frequencies is expected to be small. The surface roughness was measured by an automated laser profilometer. This instrument recorded the surface height profile with a 2-mm horizontal sampling intervals and total length of 2-m transect. For each field site, there were several surface roughness profiles, ranging from three to six profiles. Using the surface roughness profile measurements, the AIEM model input roughness parameters—the surface rms height and correlation length can be derived. Table I summarizes the soil moisture and surface roughness conditions of this field experimental data. Due to the complexity of natural surfaces, it should be recognized that there are many issues involved in the comparison the theoretical surface microwave models with the field experimental data. The theoretical models assume isotropic roughness properties and homogenous dielectric half-space. However, the natural soil has strong heterogeneities in the 3-D spatial variations of the soil characteristics including surface roughness, soil structure, and soil moisture content. It leads to the difficulty or large uncertainties in comparison between the theoretical model predictions of the field experimental data. Therefore, we performed the following two tasks in the direct comparisons between the experimental data and AIEM simulations. 1) Task-1: The input surface roughness parameters for AIEM model are derived by the averaged surface rms height and the correlation length from the roughness profiles at each field site. 2) Task-2: The input surface roughness parameters for AIEM model are from the one of the roughness profiles at each field site that provides the minimum the root mean square
TABLE II SUMMARY OF THE RMSES IN THE DIRECT COMPARISONS BETWEEN AIEM MODEL CALCULATIONS FROM THE TWO SURFACE ROUGHNESS PARAMETERS INPUT TASKS AND THE FIELD EXPERIMENTAL DATA
error (rmse) between the measurements and AIEM simulated emission signals for all frequency and polarizations. The aim is to evaluate: 1) the sensitivity of the surface roughness parameters that obtained from the field measurements and 2) the AIEM capability in applying to the frequencies considered in this study. Table II summarizes the number of available measurements at each frequency and the two test results in terms of the rmses for the emissivity at each frequency from all test sites and for overall from all frequencies and test sites. The rmses for the emissivity by using the mean surface roughness parameters at each frequency are ranged from 0.024–0.042 (v-polarization) and 0.048–0.056 (h-polarization). On the other hand, the rmses by using the “best fit” roughness parameters from the available roughness profiles at each site are ranged from 0.022–0.031 (v-polarization) and 0.036–0.046 (h-polarization) at each frequency. In comparisons of the results from the above two tests, it is noted that the significant better accuracies were obtained by using the “best fit” roughness parameters than that obtained from using average roughness parameters for all frequencies and polarizations except for v-polarization at 10.65 GHz. Fig. 1 shows the comparisons for all four frequencies of the surface emissivity that measured by the ground radiometer ( axis) and that calculated by AIEM model ( axis) using the “best fit”
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ability to produce the accurate simulations for the surface emission signals for over the frequency range of this study.
III. NUMERICAL SIMULATIONS OF THE MULTIFREQUENCY AND POLARIZATION CHARACTERISTICS OF THE SURFACE EMISSION SIGNALS UNDER AMSR-E Fig. 1. Comparisons of the surface emissivity from the INRA experimental data (x axis) with AIEM model predictions (y axis) using the “best” roughness parameters from the available roughness profiles at each site at the frequencies of 5.05, 10.65, 23.8, and 36.5 GHz and with incidences of 50 .
roughness parameters. The overall rmses in terms of emissivity for all frequencies are 0.026 and 0.039 for v- and h-polarizations, respectively. It has the much better accuracies than that obtained overall rmses of 0.030 and 0.051 for v- and h-polarizations by using the average surface roughness parameters. In comparison of these two task results from both individual frequency and overall results, it indicates that the mean surface roughness parameters from the limited roughness profile measurements (the profile length and number of profiles) may not represent the effective surface roughness at the footprint size of the measurements. It has been recognized that there are large uncertainties in characterization of the surface roughness properties at the size of the radiometer measurement footprint, especially the surface correlation length, from the limited surface roughness profiles. Davison et al. [22] showed both surface rms height and correlation length varying greatly with the measurement scales. Unlike the comparisons of the theoretical model with the Monte Carlo model simulated data and the well-controlled laboratory experimental data that have the well-described surface roughness and dielectric properties, the natural soil, however, commonly has strong heterogeneities in the 3-D spatial variations of the soil surface characteristics of the surface roughness parameters. In addition to the uncertainty of the surface roughness characterization at the footprint size, there exist also uncertainties to obtain the equivalent dielectric properties for AIEM model input from the limited soil moisture profile measurements at a point for each test site. For instance of ploughed soils, after dry and sunny conditions following rainfall events, large emerging clods are drying out more rapidly than hollows within the fields. The nonuniformities of soil moisture distribution both in horizontal distribution at a scale of a few centimeters and in the vertical profile are commonly found in the soil moisture measurements [23]. In recognizing these uncertainties in the field soil characterizations in both surface roughness parameters and soil moistures derived from the limited field experimental data for AIEM model input, we believe that this level of the uncertainties from the test results by using the “best” roughness parameters from the available roughness profiles at each site are possible within the uncertainties of the field experimental data. The predicted surface emissivities are well matched to that from the experimental data over a large number of observations and the characteristics of the surface emission signals in their frequency and polarization dependences. It indicates that AIEM model has
In order to evaluate and characterize the effects of roughness on surface emission signals for AMSR-E data analyses and for developing a simple surface emission model, we generated a simulated surface emission database under the sensor parameters of AMSR-E-frequencies: 6.925, 10.65, 18.7, 23.8, and 36.5 GHz, polarizations: v- and h-polarizations, and incident angle of 55 , using the AIEM model. This database covers a wide range of the volumetric soil moisture from 2% to 44% at 2% interval, and the surface roughness parameters including the rms height from 0.25–3 cm at 0.25-cm interval, and the correlation length from 2.5–30 cm at 2.5-cm interval. There are 2904 simulated emissivities at each frequency and polarization. The commonly used Gaussian correlation function was used in the simulation since it is a better approximation for high-frequency microwave measurements than that of the exponential correlation function. Surfaces with row patterns are beyond the scope of this study and need to be studied in the future. Fig. 2 shows the AIEM model simulated effective surface reflectivity ( axis) for all combinations of surface roughness and soil moisture parameters versus the corresponding Fresnel (flat surface) reflectivity ( axis) for v (top row) and h (bottom row) polarizations. The columns, from left to right in the plot, represent for the frequencies 6.925, 10.65, 18.7, 23.8, and 36.5 GHz, respectively. The departing of the effective reflectivity from the straight diagonal 1 : 1 line in Fig. 2 indicates the effect of surface roughness. The surface effective reflectivity or emissivity has frequency dependence. For the simulated the effective reflectivity with the same soil moisture and surface roughness range, its v- and h-polarization signals as shown in Fig. 2 ( axis), all decreases as frequency increases. This phenomenon agrees well with the current understanding of its frequency dependence. This dependence is mainly resulted from the characteristics of the frequency dependence of dielectric properties of liquid water. The dielectric constant for solid material is almost independent of the frequency in the microwave regions. However, that of liquid water has strong dependence on the frequency. It can be seen in Fig. 2 ( axis) that the Fresnel reflectivity in both v- and h-polarizations deceases and reduces its dynamic range when the frequency increases for the same soil moisture and surface roughness range. On the other hand, the effects of surface roughness on the effective reflectivity are quite consistent—increasing in v-polarization and decreasing in h-polarization at similar absolute magnitudes at each frequency. There is only a weak frequency dependence observed on the effects of the surface roughness. This suggests that the frequency dependence of the surface effective reflectivity or emissivity is mainly due to the frequency dependence of surface dielectric properties rather than the effects of the surface roughness. For the effect of surface roughness on h-polarization at all frequencies under considerations as shown in Fig. 2 (the bottom
SHI et al.: PARAMETERIZED MULTIFREQUENCY-POLARIZATION SURFACE EMISSION MODEL
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Fig. 2. Frequency and polarization characteristics of surface roughness effects on the surface effective reflectivity under AMSR-E sensor configurations at 55 with AIEM simulated surface effective reflectivity (y axis) and the corresponding Fresnel reflectivity (x axis) with v-polarization on the top row and h-polarization at the bottom row.
row), it always reduces the reflected energy or increase the emission in comparison with that from a flat surface. This phenomenon is consistent with the current understanding and predictions of the current semiempirical models [1]–[6]. However, the effects of surface roughness on v-polarization actually increase the surface effective reflectivity or decreases the emissivity compared to that of a flat surface as shown in Fig. 2 (top row). It indicates that the effect of roughness on the effective reflectivity or emission at large incident angles differs in both magnitude and direction (referring to increase or decrease the effective reflectivity) for the two different polarizations. The same phenomenon was also observed for L-band surface emission study at large incidences [6]. As a result, this phenomenon significantly affects on the description of the relationship of the surface emission signals between v- and h-polarizations, especially on the polarization ratio v/h measurements. It is clearly indicated that the polarization ratio of the effective reflectivity actually enhances, rather than reduces, the effect of the surface roughness. It will result in soil moisture estimation error when using both v- and h-polarization measurements if the surface roughness effects were assumed to be same in both polarizations as the current available semiempirical model predictions. For further supporting our observations obtained from the AIEM model simulated data, we show Fig. 3—a same type of the plot as in Fig. 2 using the ground radiometer experimental data for all frequencies at 50 that was obtained from INRA campaign in 1993 as described in last section. It can be seen that the most of the measurements are greater in v-polarization [Fig. 3 (left)] and smaller in h-polarization [Fig. 3 (right)] than the corresponding Fresnel’s reflectivity for flat surfaces. These measurements agree well with the AIEM model simulations on the effects of surface roughness on the surface effective reflectivity at the different polarizations. There are also a few measurements that are below 1 : 1 line [Fig. 3 (left)] for v-polarization and above 1 : 1 line [Fig. 3 (right)] for h-polarization. These points might result from the uncertainties in processing the ground surface soil moisture measurements to Fresnel’s reflectivity at the footprint scale and in processing the radiometer measurements to the measured surface emission signals. Through our evaluation of the effects of surface properties on the microwave emission signals simulated by AIEM model and the field experimental data at different polarizations and frequencies, there are two important characteristics of the effective reflectivity at large incidence angle (larger than 50 ) and high frequencies can be summarized as follows.
Fig. 3. Polarization characteristics of surface roughness effects on the surface effective reflectivity (y axis) from the ground radiometer measurements from INRA experimental data and the corresponding Fresnel reflectivity (x axis) at 50 and the frequencies of 5.05, 10.65, 23.8, and 36.5 GHz.
• The effective reflectivity always decreases as frequency increases due to the frequency dependence of the surface dielectric properties that results from its liquid water properties. • The surface roughness effects on the effective reflectivity differ at different polarizations for the same surface roughness parameters in both the magnitudes and directions that refer to increase (in v-polarization) or decrease (in h-polarization) the surface effective reflectivity. These two important characteristics provide the guidance for our evaluation the current available semiempirical models and the development a newly proposed surface emission model for their essential frequency and polarization characteristics of the surface emission signals. IV. SELECTION OF THE SIMPLE MODEL FORM In order to develop a simple but accurate surface emission model for image based analysis of microwave remote sensing applications, we consider the following criteria in evaluation of the current available semiempirical models and our own model development. They include the following: • the simple but accurate in each frequency and polarization that can be easily used in the remote sensing applications; • the relative accuracy in description of the dependence of surface emissivity on frequency and polarization; • the roughness description that can be simply related to the tradition surface roughness measurements such as surface rms height and correlation length; • the surface roughness property can be considered as a constant in the time series measurements when it does not change. In other words, the surface roughness parameter
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does not vary with the surface dielectric properties so that its estimation from one soil moisture condition can be used to the other moisture condition. The task is to evaluate and select an appropriate model form that can be best used to take into account above considerations. A. Selection of the Test Model Forms During past years, the several semiempirical models have been developed with the different forms [1]–[6], [24]. That all can match the measurements with the certain accuracies. Depending on the range of the experimental data (frequencies, polarization, incidence angles and surface soil roughness and dielectric properties) and how these models were deployed, these models may work reasonable well in that specific condition. The most of them was developed from the low-frequency measurements (L-band). They need to be evaluated for application under higher frequencies and large incidence angle of AMSR-E sensor parameters. In this study, we evaluated three model forms that could be possibly applied AMSR-E measurements based on above criteria. The first model form is the Q/H model [25], [26]. It is a most commonly used semiempirical model that describes the bare surface emission as a function of the surface roughness and dielectric properties as (3) The roughness parameter Q describes the energy emitted in orthogonal polarization due to the surface roughness effect. The roughness parameter H describes the effect of surface roughness to decrease surface effective reflectivity as the frequency increasing. The surface roughness parameters Q and H in (3), commonly, are often with the values between 0 and 1 to justify the surface roughness effect on the reflectivity. They are usually determined empirically from the experimental data for a given frequency and incidence angle and often called “effective roughness” [1]–[5]. The other semiempirical models [2], [3], [5] are basically developed by modifying the Q/H model. They all assumed Q with the different functional forms of H parameter. The polarization dependence from the effect of the surface roughness might be corrected by setting H parameter with the polarization dependence. Therefore, we select it as the second test model form and simply call it as H model H
(4)
The third model form is the one we proposed in this study Q
Q
(5.1)
As we noticed that for the emissivity, it can be simply derived Q
Q
(5.2)
is the Fresnel transmittivity. It can also be where considered as the modification from the Q/H model with H and setting Q parameters with the polarization dependence for correcting the effects of surface roughness at different polarizations in comparison of the Q/H model. We simply call it as Q
model. In the Q model, the roughness parameters Q works in both forms of the effective reflectivity and emissivity. The reasons why we select this form as follows. The roughness effect on the coherent components as shown in (1) is represented by H parameter (as a function of ks and ) while its effect on the noncoherent components is a function of frequency, polarization, and the surface roughness properties— and (the correlation length). At the low frequency (L-band), the coherent components are the dominant term in the effective reflectivity or emission signals and is the major surface roughness effect increases, for a given incidence. As the surface roughness the effective reflectivity of the coherent component decreases very quickly while that of the noncoherent component increases so that the noncoherent components are dominant scattering sources at high frequencies. Therefore, there is a significant behavior difference of the surface roughness effects on the effective reflectivity at the low and high frequencies. It indicates that the H parameter may not play an important role in modeling its microwave surface emission signals for high-frequency microwave signals. For the effects of surface roughness on the surface emission of the noncoherent components, the form of (5) corresponds the noncoherent components in (1) quite well with the first and second terms representing the overall contributions from the its own and the orthogonal polarizations, respectively. Therefore, we select the form of (5). In the above three selected model forms, the effects of the surface roughness are all described by two roughness parameters with the Q and H for both polarizations, H and Q for each polarization, respectively. Therefore, they all have the equal complexity in modeling the effective reflectivity or emission signals. In the following subsection, we will evaluate the above three initial selected model forms by using AIEM simulated and the field experimental data to select an appropriate model form that can be best used to describe the criteria considered in this study.
B. Evaluation of the Selected Model Forms With the AIEM Simulated and the Field Experimental Data Figures Through analysis using the simulated database by AIEM model for a wide range of surface soil moisture and roughness parameters, we performed the evaluations on the three initial selected model forms. Two tests were carried out. The first test is to evaluate the overall fitness to the selected model forms in terms of the average surface roughness parameters determined from a range of soil moisture conditions. This was done through the regression analyses using a group of the simulated effective reflectivity and the corresponding Fresnel reflectivity with the different dielectric properties to determine the roughness parameters Q H H , and Q for a given surface roughness properties—rms height and correlation length. In principle, they should vary only with the different surface roughness properties. Fig. 4 (top row) shows the comparison between the AIEM simulated surface effective reflectivity ( axis) and the corresponding H model calculations ( axis) of v-polarization (left), and h-polarization (middle), and v/h ratio (right) for a wide range surface roughness and soil moisture conditions for AMSR-E sensor parameter at 10.6 GHz. In comparison with
SHI et al.: PARAMETERIZED MULTIFREQUENCY-POLARIZATION SURFACE EMISSION MODEL
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Fig. 4. Comparison between the AIEM simulated surface effective reflectivity (x axis) and the corresponding H (top row), Q/H (middle row), and Q (bottom row) model calculations for v-polarization (left), h-polarization (middle), and the v/h ratio (right) for a wide range surface roughness and soil moisture conditions for AMSR-E sensor parameter at 10.6 GHz and 55 incidence.
AIEM model predictions, we can see that the H model has the worst accuracies with the absolute rmses of 0.0114, 0.0075, and 0.0502 for v-, h-polarizations, and v/h ratio, respectively. The accuracy of the v/h ratio describes the relationships of surface emission signals between the different polarizations. For h-polarization, it worked fairly well. However, the large errors indicate that it is poorly predicting the surface effective reflectivity in v-polarization and the v/h ratio. This is mainly due to this model form does not consider the emission signal contributions from the energy emitted in the orthogonal polarization. At the low frequency such as at L-band, the dominant term in the surface effective reflectivity is the coherent reflection—the first term in (1). The noncoherent terms in (1) may not significantly contribute to the surface effective reflectivity. At the low frequency, therefore, the surface effective reflectivity is more closely related to the Fresnel’s reflectivity in a linear is quite reasonable. relationship and the assumption of Q However, at the high frequencies, the noncoherent terms in (1) are the dominant terms to the surface effective reflectivity. The energy emitted from the orthogonal polarization significantly affects the surface emission signals. Fig. 4 (middle row) shows the comparisons for the Q/H model form. In comparison with AIEM model predictions, the Q/H model form worked better than the H model form with the rmses of 0.0052, 0.0063, and 0.0091 for v-, h-polarizations, and v/h ratio, respectively. The error is more significant in v-polarization than that in h-polarization because of the larger dynamic range of the effective reflectivity in h-polarization than that in v-polarization. Its predictions in v-polarization have the system error—that always underpredicts the effective reflectivity for the large effective reflectivity cases as shown in Fig. 4 (middle row left). The Q/H model provided the second best accuracies
among the three selected model forms. It, especially, improved the accuracies in v-polarization (two times better) and the v/h ratio (five times better) predictions in comparison with the H model form. It is mainly because the Q/H model has considered the contribution of the energy emitted from the orthogonal polarization. The error resulted from the Q/H model is mainly due to its assumption of the equal effect of the surface roughness in both v- and h-polarizations. Fig. 4 (bottom row) shows the comparisons for Q model. In comparison with the AIEM model predictions, the Q model provided the best accuracy from the three selected test models. The absolute rmses are 0.0010, 0.0011, and 0.0034 for v-, h-polarizations, and v/h ratio, respectively. They are about five times better than that from the Q/H model. This is because this model form is correspond the noncoherent components in (1) quite well with the first and second terms in (6) representing the overall contributions from its own and the orthogonal polarizations. It also sets Q parameters with the polarization dependence for correcting the effects of surface roughness at different polarizations in comparison of the Q/H model. The second test is to evaluate the three selected model forms for their applications in the time series data analyses. One of the advantages of the change detection analyses is that the surface roughness properties at each pixel of the satellite observations can be commonly considered as a constant over a certain time frame. That is, the surface roughness parameter Q H H and Q should not vary significantly with the surface dielectric properties so that they can be considered as a constant. For this test, we used the dry soil moisture condition—2% of the volumetric soil moisture to determine the roughness parameters of Q H H and Q then applied them to the other moisture conditions (from 4% to 44%) to calculate the surface emission
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TABLE III SUMMARY OF THE ABSOLUTE RMSE OF EMISSIVITIES FOR THE THREE SELECTED MODEL FORMS FROM USING INRA’S EXPERIMENTAL DATA
Fig. 5. Relationships of the s/l ratio to the roughness parameters Q AMSR-E sensor parameter at 10.6 GHz and 55 incidence.
signals. Therefore, it is essentially a sensitivity test of the surface roughness parameters in each selected model in terms of the evaluation of its estimation from one soil moisture condition and applying to the other moisture condition. The large error will indicate the sensitivity of the surface roughness parameters to the surface dielectric properties. Our test results indicated that the errors from H model are extremely large in v-polarization with 0.0708 and v/h ratio with 0.1919. They are far from the acceptable limit for the applications. Mo and Schmugge [2] showed that the H parameters had to be justified to the soil moisture conditions. It indicates that the determined surface roughness parameters H are still greatly affected by the surface dielectric properties. The Q/H model determined surface roughness has a much less sensitivity to the different surface dielectric properties than that of H model. However, its errors have got in level with 0.0168, 0.0171, and 0.0219 for v, h, and v/h, respectively. The errors of Q model under this test are with the rmses are 0.0042, 0.0048, and 0.0063 for v-, h-polarizations, and the level. It inv/h ratio, respectively. The errors are still in dicates the surface roughness parameters by the Q model have no significant dependence on the soil moisture conditions. Similar to the test results obtained from the first test as in Fig. 4, they also show that the order of the three selected test model forms are the Q Q H, and H models in respecting to the best accuracy. In addition to the tests by using AIEM simulated data, we also evaluated the selected model forms using the ground radiometer datasets from INRA’s experiment in 1993. This was done by using the Fresnel’s reflectivity calculated at each frequency from the measured soil texture, bulk density, and the averaged soil moisture and temperature values from the measurements at the surface, and at 0.5- and 1-cm depths in the vertical profile at each field site. The roughness parameters of Q and H were determined by the surface emission measurements at each frequency and both polarizations. The parameters for H and Q were determined at each frequency and polarization. Table III summarizes the test results in terms of the rmses for each selected model forms at each frequency and polarization and their overall rmses. Again, it can be seen that the Q model provides the highest accuracies for the most of the frequencies and polarizations except the H model at C-band h-polarization and the Q/H model at 10.65 GHz v-polarization with a slight better results, but negligible, than that from the Q model. The overall rmses of v-polarization from all frequencies are 0.023, 0.029, and 0.021 and that for h-polarization are 0.032, 0.030, and 0.027 for the Q H H and Q models, respectively. Through above evaluations with both the AIEM simulated and the field experimental data, it is clearly indicated that the Q model is the best model form among the three selected model
at
TABLE IV PARAMETERS FOR CALCULATION OF THE SURFACE ROUGHNESS PARAMETERS Q FROM THE s=l RATIO MEASUREMENT AT 10.65 GHz IN (6)
forms. It has the highest accuracies and the determined surface roughness parameters have no significant effect of the soil moisture. Therefore, we select the Q model to describe the surface emission signals since it can be applied to the measurements from both a single-pass and the multipass measurements for the time series analysis. C. Surface Roughness Parameterization for Q Model Through analyses using the simulated database generated by AIEM model for a wide range of surface soil moisture and roughness parameters, we evaluated the relationships of the Q parameters with the commonly used surface roughness properties— (rms height) and (correlation length). We found that the surface roughness parameters Q are highly correlated —the ratio of rms height to to a single roughness property: correlation length. This agrees well with the geometric optical (GO) model predictions for very rough surfaces—scattering at high frequency is controlled mainly by the rms surface slope for the Gaussian correlation that is defined as function [14], [15]. Fig. 5 shows the relationships between the ( axis) at 10.65 GHz roughness parameters Q ( axis) and and 55 with Q on the left and Q on the right. As we can see increases. that the Q parameters increases as the ratio of Both Q and Q can be described as the nonlinear functions of ratio. But they differ for the different polarizations with the ratio. Through regression the Q larger than Q for a given analysis, we can generally describe their relationships at each frequencies and polarization as (6) and depend on the frequency and poThe parameters larization for the given AMSR-E’s incidence angle. They can be obtained by the multivariable linear regression analyses from the AIEM simulated database. Table IV gives the parameters of and for different polarizations at AMSR-E’s 10.65 GHz. The rmses in comparison of the roughness parameters of Q derived from the AIEM model simulated database with that calparameters are 0.0012 and 0.0051 for culated using (6) and Q and Q , respectively. The error is extremely small.
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Fig. 6. Relationship of the surface roughness parameters Q at 10.65 GHz (x axis) to that at the other frequencies (y axis) on each column of the plot. The top and bottom rows are for v- and h-polarizations.
Through comparisons of Q parameters at different frequencies, it was also found that the frequency dependence of the ratio. roughness parameter Q are quite small for a given There is only a slight difference between the Q parameters at the different frequencies. To demonstrate this observation, we plot the relationships between Q at 10.65 GHz ( axis) and that at the other frequencies ( axis) in Fig. 6 with Q (top row) and Q (bottom row). From left to right, they are for 6.925, 18.7, 23.8, and 36.5 GHz, respectively. There are almost no differences of Q at 10.65 GHz with that at the other frequencies except for that at 36.5 GHz. For Q , however, there is only a slight change at the different frequencies in comparison with that at 10.65 GHz. For the general trend, Q decreases very slightly while Q increases as the frequency increases. This characteristic of the frequency dependence of the surface roughness parameter also agrees well with the GO model that predicts that the bistatic scattering coefficient has no frequency dependence. Furthermore, the frequency dependence of the roughness parameters of Q can be well characterized with a linear relation. In other words, if we know Q at one frequency, that at other frequencies can be calculated. The following relations between Q at 10.65 GHz and that at other frequencies are provided for AMSR-E sensor configuration at 55 : GHz
(7)
The parameters and depend only on the polarization and the Q at the frequency is going to be calculated. Table V lists and parameters that are needed for AMSR-E sensor the frequencies considered in this study. From the above developments, we have fully developed a parameterized surface emission model—Q model under AMSR-E sensor configuration. For this model, the roughness parameters Q at different frequencies can be calculated using (6) at 10.65 GHz with the input of a single roughness parameter and then using (7) to convert it to the other frequencies. The other required inputs are the Fresnel’s reflectivity or transmitivity in (5.1) or (5.2) can be calculated from the surface dielectric properties. Table VI shows the rmses in comparison of the surface effective reflectivity using the Q model calculated by above procedure with that simulated by AIEM model at each AMSR-E’s frequencies and polarizations. The rmses for predictions of and are all extremely small at low end
TABLE V PARAMETERS FOR CALCULATION OF THE SURFACE ROUGHNESS PARAMETERS Q (f ) FROM Q (10.65 GHz) IN (7)
TABLE VI SUMMARY OF THE COMPARISONS OF THE Q MODEL WITH THE AIEM MODEL PREDICTIONS IN THE SURFACE EFFECTIVE REFLECTIVITIES AT DIFFERENT FREQUENCY AND POLARIZATION
of level with the maximum rmse of 0.0023 for C-band h-polarization. It is clearly indicated that the errors resulted in (6) and (7) are not significant. Therefore, the newly developed Q model is very simple and suitable for the microwave remote sensing applications with the negligible error in comparison with AIEM model simulations. V. CONCLUSION The recent improvement in the AIEM model has made it possible to simulate surface emission signals for the very wide range of rough surface parameters or for the high-frequency observations. It allows more accurate calculation of surface emission signals over a wide range of surface dielectric, roughness, and sensor frequencies. We evaluated its capability in simulating high-frequency and high-incidence surface emission signals and compared with the large amount of the field observation data. In comparisons of the AIEM model simulation with the INRA93’s field experimental data, the results showed a very good agreement with the rmses of 0.026 and 0.039 for v- and h-polarizations when using the selected roughness profile measurement at each field site. It was also found that there are large uncertainties in characterization of the surface roughness properties at the size of the radiometer measurement footprint, especially the surface
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correlation length, from the limited number of surface roughness profiles. The comparison with the average roughness parameters from the limited available roughness profile measurements at each field site, actually, showed the worse accuracies with the rmses of 0.030 and 0.051 for v- and h-polarizations, respectively. It indicates that the mean surface roughness parameters from the only limited roughness profile measurements may not represent the effective surface roughness at the footprint size of the measurements. Further study on how to characterize the surface roughness properties at the scale of the radiometer measurement footprint is needed. With the confirmed confidence on AIEM model, we developed a parameterized multifrequency-polarization surface emission model—the Q model for a wide range of surface dielectric and roughness properties for the applications in analysis of the passive microwave satellite measurements from AMSR-E. This model takes into account the effects of the surface roughness on the emission signals through the roughness variable Q at different polarizations— . We evaluated our newly proposed Q model, along with two commonly used semiempirical model forms—the Q/H and H models, compared with both the AIEM simulated and field experimental data. The test results indicate that the Q model form gives the highest accuracy and the most suitable for the high-frequency and high-incidence AMSR-E data analysis among the three tested model forms. In comparison with the theoretical model form given in (1), the H model form does not consider the emission signal contributions from the energy emitted in the orthogonal polarization that significantly affects the surface emission signals at the higher frequencies because the noncoherent terms in (1) are the dominant terms in the surface effective reflectivity. As a result, the H model form performs poorly in predicting the surface effective reflectivity at v-polarization and the v/h ratio. The main weakness of the Q/H model form is due to its assumption of the equal effect of the surface roughness in both v- and h-polarizations. It causes that the predictions at v-polarization have the system error—that always underpredicts the effective reflectivity for the cases of large effective reflectivity. The Q model form corresponds the noncoherent components in (1) quite well with the first and second terms in (5) representing the overall contributions from its own and the orthogonal polarizations. It also sets Q parameters with the polarization dependence for correcting the effects of surface roughness at different polarizations unlike the Q/H model. Through the evaluations with both the AIEM simulated and the field experimental data, it is clearly indicated that the Q model offers the best accuracy and the determined surface roughness parameters having no significant effect of the soil moisture. Through evaluation of the Q parameters and their relationship to the commonly used surface roughness properties— (rms height) and (correlation length) using the AIEM model simulated database, it was found that the Q can be simply described as a single surface roughness parameter—the ratio of the surface rms height and correlation length with a nonlinear form (6). In addition, the characteristics of the frequency dependence of Q parameters can be characterized as there is extreme weak frequency dependence at v-polarization. For h-polarization, however, there is a noticeably slight change at different frequencies. Their frequency dependence can be
generally described as a linear function (7). The comparison of the simulations by the Q and AIEM models indicated that the . emissivity error is extremely small at the magnitude of In summary, the newly developed Q model provides a simple and accurate connection between the surface emission at different frequencies and polarizations, as well as the commonly used surface roughness parameter measurements. It can be applied to the measurements from both a single-pass and the multipass measurements for the time series analyses. The simple Q surface emission model developed in this study should be very useful in modeling, improving our understanding, analyses, and prediction of the AMSR-E measurements. REFERENCES [1] J. R. Wang, P. E. O’Neill, T. J. Jackson, and E. T. Engman, “Multifrequency measurements of the effects of soil moisture, soil texture, and surface roughness,” IEEE Trans. Geosci. Remote Sens., vol. GE-21, no. 1, pp. 44–51, Jan. 1983. [2] T. Mo and T. J. Schmugge, “A parameterization of the effect of surface roughness on microwave emission,” IEEE Trans. Geosci. Remote Sens., vol. GE-25, no. 1, pp. 47–54, Jan. 1987. [3] U. Wegmüller and C. Mätzler, “Rough bare soil reflectivity model,” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1391–1395, May 1999. [4] C. Prigent, J.-P. Wigneron, W. B. Rossow, and J. R. Pardo-Carrion, “Frequency and angular variations of land surface microwave emissivities: Can we estimate SSM/T and AMSU emissivities from SSM/I emissivities?,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5, pp. 2373–2386, Sep. 2000. [5] J. P. Wigneron, L. Laguerre, and Y. H. Kerr, “A simple parameterization of the L-band microwave emission from rough agricultural soil,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 8, pp. 1697–1707, Aug. 2001. [6] J. Shi, K. S. Chen, Q. Li, T. J. Jackson, P. E. O’Neill, and L. Tsang, “A parameterized surface reflectivity model and estimation of bare surface soil moisture with L-band radiometer,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 12, pp. 2674–2686, Dec. 2002. [7] T. J. Jackson and T. J. Schmugge, “Vegetation effects on the microwave emission of soils,” Remote Sens. Environ., vol. 36, pp. 203–212, 1991. [8] T. J. Jackson, D. M. Le Vine, A. Y. Hsu, A. Oldak, P. J. Starks, C. T. Swift, J. Isham, and M. Haken, “Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains Hydrology Experiment,” IEEE Trans. Geosci. Remote Sens., vol. 27, no. 5, pp. 2136–2151, Sep. 1999. [9] E. G. Njoku and L. Li, “Retrieval of land surface parameters using passive microwave measurements at 6–18 GHz,” IEEE Trans. Geosci. Remote Sens., vol. 30, no. 1, pp. 79–93, Jan. 1999. [10] E. Njoku, T. Jackson, V. Lakshmi, T. Chan, and S. V. Nghiem, “Soil moisture retrieval from AMSR-E,” IEEE Trans. Geosci. Remote. Sens., vol. 41, no. 2, pp. 215–229, Feb. 2003. [11] S. Paloscia, G. Macelloni, E. Santi, and T. Koike, “A multifrequency algorithm for the retrieval of soil moisture on a large scale using microwave data from SMMR and SSM/I Satellite,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 8, pp. 1655–1661, Aug. 2001. [12] L. Jiang, J. Shi, S. Tjuatja, and K. S. Chen, “Study of snow water equivelance inversion technique with simulating model,” Proc. SPIE, vol. 5654, pp. 157–166, Nov. 2004. [13] A. T. C. Chang, N. Grody, L. Tsang, A. Basist, B. Goodison, A. Walker, T. Carroll, R. Armstrong, E. Josberger, and C. Sun, “Algorithm Theoretical Basis Document (ATBD) for AMSR-E snow water equivalent algorithm,” NASA Goddard Space Flight Center, Greenbelt, MD, Nov. 1997. [14] L. Tsang, J. A. Kong, and R. T. Shin, Theory of Microwave Remote Sensing. New York: Wiley, 1985. [15] F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive. Reading, MA: Addison-Wesley, 1982, vol. 2, Radar Remote Sensing and Surface Scattering and Emission Theory. [16] A. K. Fung, Microwave Scattering and Emission Models and Their Applications. Norwood, MA: Artech House, 1994. [17] K. S. Chen, T. D. Wu, L. Tsang, Q. Li, J. Shi, and A. K. Fung, “The emission of rough surfaces calculated by the integral equation method with a comparison to a three-dimensional moment method simulations,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 1, pp. 90–101, Jan. 2003.
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[18] K. S. Chen, T. D. Wu, and A. K. Fung, “A note on the multiple scattering in an IEM model,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 249–256, Jan. 2000. [19] T. D. Wu, K. S. Chen, J. Shi, and A. K. Fung, “A transition model for the reflection coefficient in surface scattering,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 9, pp. 2040–2050, Sep. 2001. [20] J. C. Calvet, J. P. Wigneron, A. Chanzy, S. Raju, and L. Laguerre, “Microwave dielectric properties of a silt-loam at high frequencies,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 3, pp. 634–642, May 1995. [21] M. C. Dobson, F. T. Ulaby, M. T. Hallikainen, and M. A. El-Rayes, “Microwave dielectric behavior of wet soil, Part II: Dielectric mixing models,” IEEE Trans. Geosci. Remote Sens., vol. GRS-23, no. 1, pp. 35–46, Jan. 1985. [22] M. W. J. Davidson, T. L. Toan, F. Mattia, C. Satalino, T. Manninen, and M. Borgeaud, “On the characterization of agricultural soil roughness for radar remote sensing studies,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 630–640, Mar. 2000. [23] W. Jean-Pierre and J. Shi, “Modeling the soil microwave emission,” in Radiative Transfer Models for Microwave Radiometry, C. Mätzler, Ed. StevenAge, U.K.: Inst. Elect. Eng., 2005. [24] S. S. Saatchi, E. G. Njoku, and U. Wegmüller, “Synergism of active and passive microwave data for estimating bare soil surface moisture,” in Proc. Passive Microwave Remote Sensing of Land-Atmosphere Interactions, ESA/NASA Int. Workshop, 1995, pp. 205–224. [25] B. J. Choudhury, T. J. Schmugge, A. Chang, and R. W. Newton, “Effect of surface roughness on the microwave emission from soil,” J. Geophys. Res., vol. 84, no. C9, pp. 5699–5706, 1979. [26] J. R. Wang and B. J. Choudhury, “Remote sensing of soil moisture content over bare fields at 1.4 GHz frequency,” J. Geophys. Res., vol. 86, pp. 5277–5282, 1981.
Jiancheng Shi (SM’02) received the B.A. degree from the University of Lanzhou, Lanzhou, China, and the M.A. and Ph.D. degrees in geography from the University of California, Santa Barbara (UCSB), in 1982, 1987, and 1991, respectively. He then joined the Institute for Computational Earth System Sciences at UCSB as a Research Scientist. His research interests are microwave modeling snow and soil signatures, image processing and analysis, and inversion models for retrieving physical parameters from remote sensing data.
Lingmei Jiang (S’05) was born in Zhejiang, China on October 31, 1978. She received the B.S. degree in agricultural meteorology from the Nanjing Institute of Meteorology, Nanjing, China, and the Ph.D. degree in geography from Beijing Normal University, Beijing, China, in 2000 and 2005, respectively. Her research interests include microwave remote sensing and modeling and retrieval of snow and soil properties.
Lixin Zhang received the B.A. degree from the University of Lanzhou, Lanzhou, China, and the M.A. and Ph.D. degrees in geography from the Institute of Glaciology and Geocryology, China, in 1988, 1991, and 2000, respectively. He then joined the Research Center for Remote Sensing and GIS, Beijing Normal University, Beijing, China, as a Professor. His research interests are physical properties of frozen soil, and inversion algorithms or criteria for monitoring physical parameters of soil from remote sensing data.
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Kun-Shan Chen (S’89–M’90–SM’98) received the B.S. degree from the National Taiwan Institute of Technology, Taipei, Taiwan, R.O.C., in 1985, and the M.S. and Ph.D. degrees from the University of Texas, Arlington, in 1987 and 1990, respectively, all in electrical engineering. From 1985 to 1990, he was with the Wave Scattering Research Institute, University of Texas. In 1992, he joined the faculty of the Center for Space and Remote Sensing Research, National Central University, Chung-Li, Taiwan, R.O.C., where he is now a Professor and Director. He has joint appointments at the Institute of Space Sciences and Institute of Communication Engineering at the same university. His research activities involve in the areas of microwave remote sensing, image processing and analysis for satellite and aircraft remote sensing data, radio and microwave propagation, and scattering from terrain and ocean with applications to remote sensing and wireless communications. He has authored three book chapters, over 60 refereed journal papers, and over 100 conference papers in the areas of remote sensing and wave scattering and propagation. He has been the Editor-in-Chief of Journal of Photogrammetry and Remote Sensing since 2001 and is on the Editorial Board of the Journal of Electromagnetic Waves and Applications and Transactions of the Aeronautical and Astronautical Society of the Republic of China. He serves as technical consultant at several national research agencies in areas of satellite remote sensing, radar, and radio techniques. Dr. Chen was the recipient of the 1993 Young Scientist Award from the International Union of Radio Science (URSI) and has received numerous research awards from the National Science Council of Taiwan since 1993. He has been an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. In 2001, he was appointed as chairman of Commission F, Taipei, China of URSI. He is a member of the Electromagnetic Academy. He was a Technical Chairman of PIERS 1999, held in Taipei, Taiwan, R.O.C.
Jean-Pierre Wigneron (M’97–SM’03) received the engineering degree from SupAéro, ENSAE, Toulouse, France, and the Ph.D. degree from the University of Toulouse, in 1993. He is currently a Research Scientist and Head of the remote sensing group at the Institut National de la Recherche Agronomique, Ecologie fonctionnelle et Physique de l’Environnement (INRA EPHYSE), Bordeaux, France. His research interests are in microwave remote sensing of soil and vegetation, radiative transfer, and data assimilation. He has developed microwave models and soil moisture retrieval approaches in the framework of the Soil Moisture and Ocean Salinity (SMOS) Mission. EPHYSE was selected within the Expert Support Laboratory (ESL) developing the Level-2 SMOS inversion algorithm. He was Principal and Co-Investigator of several ground and airborne international campaigns in the field of microwave remote sensing. He is a member of the editorial board of Remote Sensing of the Environment.
André Chanzy received the Ph.D. degree in physics of the environment from the Institut National Agronomique Paris-Grignon, Paris, France, in 1991. He has been with the soil science laboratory at the Avignon INRA Research Center since 1987. He currently heads the Climate Soil and Environment Laboratory. His scientific fields of interest are soil water flow modeling and the application of microwave remote sensing (active and passive) and in situ observations to infer hydrological fluxes.
