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Classification, decomposition and modeling of polarimetric SAR data has received a great deal of attention in the recent literature. The objective behind these efforts is to better understand the scattering mechanisms which give rise to the polarimetric signatures seen in SAR image data.
Two simple scattering mechanisms are fitted to polarimetric synthetic aperture radar (SAR) observations of forests. The mechanisms are canopy scatter from a reciprocal medium with azimuthal symmetry and a ground scatter term that can represent double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants or Bragg scatter from a moderately rough surface, which is seen through a layer of vertically oriented scatterers. The model is shown to represent the behavior of polarimetric backscatter from a tropical forest and two temperate forest sites by applying it to data from the National Aeronautic and Space Agency/Jet Propulsion Laboratory's Airborne SAR (AIRSAR) system. Scattering contributions from the two basic scattering mechanisms are estimated for clusters of pixels in polarimetric SAR images. The solution involves the estimation of four parameters from four separate equations. This model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem. The model is used to develop an understanding of the ground-trunk double-bounce scattering that is present in the data, which is seen to vary considerably as a function of incidence angle. Two parameters in the model fit appear to exhibit sensitivity to vegetation canopy structure, which is worth further exploration. Results from the model fit for the ground scattering term are compared with estimates from a forward model and shown to be in good agreement. The behavior of the scattering from the ground-trunk interaction is consistent with the presence of a pseudo-Brewster angle effect for the air-trunk scattering interface. If the Brewster angle is known, it is possible to directly estimate the real part of the dielectric constant of the trunks, a key variable in forward modeling of backscatter from forests. It is also shown how, with a priori knowledge of the forest height, an estimate for the attenuation coefficient of the canopy can be obtained directly from the multi-incidence-angle polarimetric observations. This attenuation coefficient is another key variable in forward models and is generally related to the canopy density.
Fully polarimetric airborne synthetic aperture radar (AIRSAR) data, collected in Germany during the MAC Europe campaign, are calibrated using software packages developed at the Joint Research Center (JRC) in Italy for both L- and C-bands. During the period of the overflight dates, extensive ground truth was collected in order to describe the physical and statistical parameters of the canopy, the understory, and the soil. These parameters are compiled and converted into electromagnetic parameters suitable for input to the new polarimetric three-layer canopy model developed at the Wave Scattering Research Center (WSRC) at the University of Texas at Arlington. Comparisons between the theoretical predictions from the model and the calibrated data are carried out. Initial results reveal that the trend of the average phase difference can be predicted by the model, and that the backscattering ratio *shh/ svv is sensitive to the distribution of the primary branches.
During recent years signature analysis, classification, and modeling of Synthetic Aperture Radar (SAR) data as well as estimation of geophysical parameters from SAR data have received a great deal of interest. An important requirement for the quantitative use of SAR data is the accurate estimation of the backscattering coefficient sigma(exp 0). In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived from sensor position and Digital Elevation Model (DEM) data must always be considered. Especially in the airborne case, the antenna gain pattern can be an additional source of radiometric error, because the radar look angle is not known precisely as a result of the the aircraft motions and the local surface topography. Consequently, radiometric distortions due to the antenna gain pattern must also be corrected for each resolution cell, by taking into account aircraft displacements (position and attitude) and position of the backscatter element, defined by the DEM data. In this paper, a method to derive an accurate estimation of the backscattering coefficient using NASA/JPL AIRSAR data is presented. The results are evaluated in terms of geometric accuracy, radiometric variations of sigma(exp 0), and precision of the estimated forest biomass.
An algorithm based on a fit of the single-scattering Integral Equation Method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quasi-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.
In preparation for the launch of SIR-C/X-SAR and design studies for future orbital SAR, a program has made considerable progress in the development of an SAR terrain classifier and algorithms for quantification of biophysical attributes. The goal of this program is to produce a generalized software package for terrain classification and estimation of biophysical attributes and to make this package available to the larger scientific community. The basic elements of the SAR (Synthetic Aperture Radar) terrain classifier are outlined. An SAR image is calibrated with respect to known system and processor gains and external targets (if available). A Level 1 classifier operates on the data to differentiate: urban features, surfaces and tall and short vegetation. Level 2 classifiers further subdivide these classes on the basis of structure. Finally, biophysical and geophysical inversions are applied to each class to estimate attributes of interest. The process used to develop the classifiers and inversions is shown. Radar scattering models developed from theory and from empirical data obtained by truck-mounted polarimeters and the JPL AirSAR are validated. The validated models are used in sensitivity studies to understand the roles of various scattering sources (i.e., surface trunk, branches, etc.) in determining net backscatter. Model simulations of sigma (sup o) as functions of the wave parameters (lambda, polarization and angle of incidence) and the geophysical and biophysical attributes are used to develop robust classifiers. The classifiers are validated using available AirSAR data sets. Specific estimators are developed for each class on the basis of the scattering models and empirical data sets. The candidate algorithms are tested with the AirSAR data sets. The attributes of interest include: total above ground biomass, woody biomass, soil moisture and soil roughness.
Changes in the seasonal CO(sub 2) flux of the boreal forests may result from increased atmospheric CO(sub 2) concentrations and associated atmospheric warming. To monitor this potential change, a combination of remote sensing information and ecophysiological models are required. In this paper we address the use of synthetic aperture radar (SAR) data to provide some of the input to the ecophysiological models: forest type, freeze/thaw state which limits the growing season for conifers, and leaf on/off state which limits the growing season for deciduous species. AIRSAR data collected in March 1988 during an early thaw event and May 1991 during spring breakup are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off and soil freeze/thaw transitions. Finally, a CO(sub 2) flux algorithm which utilizes SAR data and an ecophysiological model to estimate CO(sub 2) flux is presented. CO(sub 2) flux maps are generated from which areal estimates of CO(sub 2) flux are derived.
A new technique for fitting a three-component scattering mechanism model to the polarimetric synthetic aperture radar (SAR) data itself, without utilizing any ground truth measurements, is presented. The three scattering mechanism components included in the model are volume scatter from randomly oriented dipoles, first-order Bragg surface scatter and a dihedral scattering mechanism for two surfaces with different dielectric constants. The model fit yields an estimate of the contribution to the total backscatter of each of the three components. The backscatter contributions can also be compared to give the relative percentage weight of each. The model fit has an equal number of input parameters (the polarimetric radar backscatter measurements) and output parameters (the backscatter parameters describing them). The model can be applied to entire images or to small areas within an image to give a first-order estimate of the relevant scattering mechanisms. The model was applied to many C-, L- and P-band Airborne SAR (AIRSAR) images of different types of terrain. Results were presented at the workshop.
An algorithm based on a fit of the single-scattering Integral Equation Method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well- managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.
The addition of interferometric backscattering pairs to the conventional polarimetric SAR data over forests and other vegetated areas increases the dimensionality of the data space, in principle enabling the estimation of a larger number of vegetation parameters. Without regard to the sensitivity of these data to vegetation scattering parameters, this paper poses the question: Will increasing the data channels as such result in a one-to-one increase in the number of parameters that can be estimated, or do vegetation and data properties inherently limit that number otherwise? In this paper, the complete polarimetric interferometric covariance matrix is considered and various symmetry properties of the scattering medium are used to study whether any of the correlation pairs can be eliminated. The number of independent pairs has direct consequences in their utility in parameter estimation schemes, since the maximum number of parameters that can be estimated cannot exceed the number of unique measurements. The independent components of the polarimetric interferometric SAR (POL/INSAR) data are derived for media with reflection, rotation, and azimuth symmetries, which are often encountered in vegetated surfaces. Similar derivations have been carried out before for simple polarimetry, i.e., zero baseline. This paper extends those to the interferometric case of general nonzero baselines. It is shown that depending on the type of symmetries present, the number of independent available measurements that can be used to estimate medium parameters will vary. In particular, whereas in the general case there are 27 mathematically independent measurements possible from a polarimetric interferometer, this number can be reduced to 15, 9, and 6 if the medium has reflection, rotation, or azimuthal symmetries, respectively. The results can be used in several ways in the interpretation of SAR data and the development of parameter estimation schemes, which will be discussed at the presentation. Recent POL/INSAR data from the JPL AIRSAR over a forested area will be used to demonstrate the results of this derivation. This work was performed by the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, under contract from the National Aeronautics and Space Administration.