Kazuki Nakamura

Forest Structure Dependency of the Relation Between L-Band

Biophysical parameters and L-band polarimetry synthetic aperture radar observation data were taken for 59 test sites in Tomakomai national forest, which is located in the northern part of Japan. Correlations between the derived sigma_HH ⁰, sigma_HV ⁰, and sigma_VV ⁰ and the biophysical parameters are investigated and yield the following results. 1) The above-ground biomass-sigma⁰ curves saturate above 50 tons/ha for sigma_VV ⁰, 100 tons/ha for sigma_HH ⁰, and over 100 tons/ha for sigma_HV ⁰ when all forest species are included in the curves. 2) The sigma_HH ⁰-above-ground biomass curve for one forest species indicates a higher saturation level than that for the other forest species. Dependence on the forest species was absent for VV polarization and low for HV polarization. 3) A simple three-component scattering model indicates that volume scattering accounts for 80%-90% when the above-ground biomass exceeds 50 tons/ha. The surface-scattering components are up to ~20% for young stands, and the volume-scattering components are down to 70%. The origin of the dependency among the forest species was examined for the sigma_HH ⁰-above-ground biomass. It is concluded that a possible cause of the dependency is the different characteristics of the stands rather than forest species

sigma^0

The Phased-Array L-Band SAR (PALSAR) aboard the Advanced Land Observing Satellite (ALOS) is capable of globally acquiring fully polarimetric data. In order to confirm the ability of L-band polarimetric synthetic aperture radar (SAR) to investigate sea ice before the ALOS launch, we conducted a field experiment using an airborne Polarimetric and Interferometric SAR (Pi-SAR) in the Sea of Okhotsk in 1999. This paper presents the analyzed results of data acquired in that experiment. The extracted polarimetric parameters of several ice types suggested that polarimetric coherences and phase differences between right-right (RR) and left-left (LL) are good candidates for discriminating ice types. The polarimetric anisotropy as well as the beta angle of the first eigenvector calculated in the polarimetric decomposition procedure are alternative parameters that are sensitive to ice type differences. Due to the low depolarization characteristics of open water, it could be discriminated from sea ice by scattering entropy in all incidence angle ranges. From the relation between ice thickness and the polarimetric parameters, we found that backscattering coefficients and vertical (VV) to horizontal (HH) backscattering ratio are highly correlated with ice thickness. Since the ratio is sensitive to ice surface dielectric constants, a simple simulation using the integral equation method surface model was conducted by using the physical parameters of typical sea ice. A two-dimensional ice thickness map was derived from an empirical relation between the VV-to-HH backscattering ratio and ice thickness.

and Biophysical Parameters

To investigate the possibilities of using dual-frequency, multipolarization synthetic aperture radar (SAR) data to monitor sea ice, we derived the relationship between various polarization characteristics and the physical parameters of sea ice. We discuss the frequency and polarization characteristics of the backscattering coefficients of sea ice and then characterize its thickness by comparing the corresponding backscattering coefficient for each polarization with the physical parameters of the ice. We first propose a methodology for classifying sea-ice types by using a polarimetric decomposition technique, before comparing an estimation of the sea-ice thickness with the corresponding dual-frequency, multipolarization SAR data. We utilized the backscattering ratio to estimate the thickness of the sea ice. This ratio canceled the effect of roughness on the backscattering. The method was validated using Pi-SAR (polarimetric and interferometric airborne SAR) observation data obtained at ground-truth sites.

Polarimetric Characteristics of sea ice in the sea of Okhotsk observed by airborne L-band SAR

This paper describes a polarimetric feature extraction method from urban areas using the POLSAR (Polarimetric Synthetic Aperture Radar) data. The scattering characteristic of urban areas is different from that of natural distributed areas. The main point of difference is polarimetric correlation coefficient, because urban areas do not satisfy property of azimuth symmetry, = 0. The decomposition technique based on azimuth symmetry can not be applied to urban areas. We propose a new model fit suitable for urban areas. The proposed model fit consists of odd-bounce, even-bounce and cross scattering models. These scattering models can represent the polarimetric backscatter from urban areas, and satisfy ¬= 0 and ¬= 0. In addition, the combination with the proposed model fit and the three component scattering model suited for natural distributed areas is examined. It is possible to apply the combined technique to POLSAR data which includes both urban areas and natural distributed areas. The combined technique is used for feature extraction of actual X-band POLSAR data acquired by Pi-SAR. It is shown that the proposed model fit is useful to extract polarimetric features from urban areas.

Observation of sea-ice thickness in the sea of Okhotsk by using dual-frequency and fully polarimetric airborne SAR (pi-SAR) data

This paper discusses the polarimetric correlation coefficient to extract the urban areas from polarimetric Synthetic Aperture Radar (POLSAR) image. For classification of POLSAR image, several methods have been proposed to extract polarimetric feature, such as Polarimetric Entropy-Alpha, three-component scattering model, Huynen parameters and so on. However, there is a possibility that the polarimetric correlation coefficient has a potential for the objective of this paper, too. In order to verify the capability of polarimetric correlation coefficient, we examine the behavior of this coefficient between the urban areas and the natural distributed areas with respect to the several polarimetric scattering models and the difference of polarization basis. Moreover, we apply the polarimetric correlation coefficient to the actual polarimetric SAR data acquired by Pi-SAR/X-SAR.

Polarimetric SAR Image Analysis Using Model Fit for Urban Structures

To investigate the suitability of synthetic aperture radar (SAR) polarization data to estimate the sea-ice thickness in early summer in Lutzow-Holm Bay, Antarctica, we compared in situ ice thicknesses with the corresponding backscattering co-efficient for each polarization and the VV-to-HH backscattering ratio. The VV-to-HH backscattering ratio was derived from data acquired by ENVISAT Advanced SAR (ASAR). This ratio is related to the near-surface dielectric constant of the sea ice, which is, in turn, related to the developing process of ice and, thus, its thickness via changes in the near-surface sea-ice salinity. The sea ice encountered in the study area is close first-year pack ice and fast ice. For these old and relatively rough sea-ice types, the VV-to-HH backscattering ratio can be expected to depend on salinity-driven changes in the near-surface dielectric constant rather than changes of the surface roughness. We applied the empirical relationships between the ice thickness and the VV-to-HH backscattering ratio with the linear and logarithm fits to ASAR data. The linear fit gave the reliable result, with an rms error being 0.08 m and a correlation coefficient being 0.91, when compared to in situ fast-ice thickness.

A study on extraction of urban areas from polarimetric Synthetic Aperture Radar image

Abstract Multi-frequency, multi-polarization airborne synthetic aperture radar (SAR) observations of sea ice in the southern Sea of Okhotsk were carried out in February 1999 in conjunction with RADARSAT SAR observations. The final goal of this study is to clarify the backscattering characteristics and to understand the scattering mechanisms of sea ice in the Sea of Okhotsk by using microwave multiparametric SAR. The airborne SAR (Pi-SAR) has two frequencies (X- and L-band) and multi-polarization (HH, VV, HV, VH) with 1.5 m (X-band) and 3.0 m (L-band) resolution. It was developed by the Communications Research Laboratory (X-band) and the National Space Development Agency of Japan (L-band). We show the frequency dependence and polarization properties of radar backscattering from sea ice. We find that it is possible to distinguish ice types by comparing backscattering from sea ice in the X- and L-bands. Investigation of the polarization characteristics at X-band was very useful for detecting the thin-ice area (e.g. nilas and gray ice).

Observation of Sea-Ice Thickness Using ENVISAT Data From LÜtzow-Holm Bay, East Antarctica

Although satellite data are known to be useful for obtaining ice thickness distribution for perennial sea ice or in stable thin sea ice areas, their use in the seasonal sea ice zone (SIZ) is still unresolved. In this study, we approached the problem of ice thickness retrieval by using L‐band Synthetic Aperture Radar (SAR). In the SIZ, ice thickness growth is closely related to ridging activity and therefore we expected surface roughness to be correlated to ice thickness. L‐band SAR is suitable for detecting such surface roughness and should be a useful tool for obtaining ice thickness distribution. To verify this correlation, we conducted shipborne electromagnetic (EM) inductive sounding and supersonic profiling observations with an icebreaker, coordinated with airborne L‐band SAR observations in the southern Sea of Okhotsk in February 2005. The surface elevation was estimated by representing the ships motion with a low‐pass filter. Backscattering coefficients correlated well with ice thickness and surface roughness, defined by the standard deviation of the surface elevation. This result sheds light on the possibility of determining ice thickness distribution in the SIZ.

CRL/NASDA airborne SAR (Pi-SAR) observations of sea ice in the Sea of Okhotsk

This paper attempts to use the polarimetric correlation coefficient for extraction of the polarimetric features of the urban areas and the natural distributed areas from Polarimetric Synthetic Aperture Radar (POLSAR) data. There is a possibility that the polarimetric correlation coefficient can reveal various scattering mechanisms of terrains based on the reflection symmetry property. In order to verify the capability of polarimetric correlation coefficient, we examined the behavior of this coefficient of the urban areas and the natural distributed areas with respect to the several polarimetric scattering models in the linear and circular polarization bases, and the difference of the polarimetric scattering characteristics between these two areas was derived. It was confirmed that the polarimetric correlation coefficient is useful to extract the polarimetric features from the actual L-band and X-band POLSAR data.

Retrieval of sea ice thickness distribution in the seasonal ice zone from airborne L-band SAR

Abstract We investigated the feasibility of using multi-polarization Synthetic aperture radar (SAR) data to estimate the thickness of undeformed first-year ice. Analysis of the radar Signatures for the C- and L-bands Showed that the correlation between the ice thickness and VV-to-HH backscattering ratio is larger than the correlation between the ice thicknesses and the backscattering coefficients. This is in part because the ice Surface Salinity and hence the Surface reflection coefficient decreases as the ice thickens. The backscattering ratio had low Sensitivity to the Small-scale ice Surface roughness for the C-band and is almost independent of roughness at L-band. Given that the ratio is most Sensitive to ice Surface dielectric constants, which depend on Salinity, we developed an algorithm for retrieving the ice thickness that is based on the backscattering ratio and on the integral-equation-method (IEM) Surface Scattering model. Comparison of the observed and estimated ice thicknesses Showed that the correlation was much better when the thicknesses were estimated using the backscattering ratio than when the backscattering coefficient was used directly. The algorithm also performed better than previous retrievals using an empirical technique.