Saibun Tjuatja

Bio: Saibun Tjuatja is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Inverse synthetic aperture radar & Scattering. The author has an hindex of 12, co-authored 78 publications receiving 536 citations. Previous affiliations of Saibun Tjuatja include University of Texas at Austin.

Laboratory measurements of radar backscatter from bare and snow-covered saline ice sheets

Abstract: We performed experiments to collect radar backscatter data at Ku (134GHz) and C bands (53GHz) over simulated sea ice at the US Army Cold Regions Research and Engineering Laboratory (CRREL) during the 1990 and 1992 winter seasons These experiments were conducted over bare saline ice grown in an indoor tank and an outdoor pond facility The radar data were calibrated using a complex vector calibration scheme to reduce systematic effects In conjunction with the radar measurements we measured ice physical properties These measurements demonstrate that the dominant backscatter mechanism for bare saline ice is surface scattering Both the copolarized and cross-polarized measurements compare favourably with the predictions of surface scattering models at two frequencies During the 1992 indoor tank experiment we applied four successive layers of snow (about 25 cm each) to the saline ice sheet after the ice thickness had reached about 12 cm The backscatter at normal incidence dropped by l5dB and t

Far-Field Interrogation of Microstrip Patch Antenna for Temperature Sensing Without Electronics

Abstract: Temperature sensing without electronics is demonstrated through wireless interrogation of passive antenna-sensors. The sensor node is equipped with an ultra-wide-band microstrip antenna as the transmitting/receiving (Tx/Rx) antenna and a microstrip patch antenna serving as the temperature-sensing element. A microstrip transmission line connecting the Tx/Rx antenna and the antenna-sensor delays the signal reflected from the sensing element and thus separated it from the background clutter. The operation principle of the wireless sensing scheme is first discussed, followed by the design and simulations of the sensor node circuitry. A digital signal processing algorithm that extracts the antenna resonant frequency from the wirelessly received signal is also described. Temperature tests were conducted to validate the performance of the wireless antenna sensor inside an oven.

A phase matrix for a dense discrete random medium : Evaluation of volume scattering coefficient

Abstract: In the derivation of the conventional scattering phase matrix of a discrete random medium, the far-field approximation is usually assumed. In this paper, the phase matrix of a dense discrete random medium is developed by relaxing the far-field approximation and accounting for the effect of volume fraction and randomness properties characterized by the variance and correlation function of scatterer positions within the medium. The final expression for the phase matrix differs from the conventional one in two major aspects: there is an amplitude and a phase correction. The concept used in the derivation is analogous to the antenna array theory. The phase matrix for a collection of scatterers is found to be the Stokes matrix of the single scatterer multiplied by a dense medium phase correction factor. The close spacing amplitude correction appears inside the Stokes matrix. When the scatterers are uncorrelated, the phase correction factor approaches unity. The phase matrix is used to calculate the volume scattering coefficients for a unit volume of spherical scatterers, and the results are compared with calculations from other theories, numerical simulations, and laboratory measurements. Results indicate that there should be a distinction between physically dense medium and electrically dense medium.

A phase matrix for a dense discrete random medium: evaluation of volume scattering coefficient

Abstract: In the derivation of the conventional scattering phase matrix of a discrete random medium, the far-field approximation is usually assumed. In this paper, the phase matrix of a dense discrete random medium is developed by relaxing the far-field approximation and accounting for the effect of volume fraction and randomness properties characterized by the variance and correlation function of scatterer positions within the medium. The final expression for the phase matrix differs from the conventional one in two major aspects: there is an amplitude and a phase correction. The concept used in the derivation is analogous to the antenna array theory. The phase matrix for a collection of scatterers is found to be the Stokes matrix of the single scatterer multiplied by a dense medium phase correction factor. The close spacing amplitude correction appears inside the Stokes matrix. When the scatterers are uncorrelated, the phase correction factor approaches unity. The phase matrix is used to calculate the volume scattering coefficients for a unit volume of spherical scatterers, and the results are compared with calculations from other theories, numerical simulations, and laboratory measurements. Results indicate that there should be a distinction between physically dense medium and electrically dense medium.

High interannual variability of sea ice thickness in the Arctic region.

Abstract: Possible future changes in Arctic sea ice cover and thickness, and consequent changes in the ice-albedo feedback, represent one of the largest uncertainties in the prediction of future temperature rise1,2. Knowledge of the natural variability of sea ice thickness is therefore critical for its representation in global climate models3,4. Numerical simulations suggest that Arctic ice thickness varies primarily on decadal timescales3,5,6 owing to changes in wind and ocean stresses on the ice7,8,9,10, but observations have been unable to provide a synoptic view of sea ice thickness, which is required to validate the model results3,6,9. Here we use an eight-year time-series of Arctic ice thickness, derived from satellite altimeter measurements of ice freeboard, to determine the mean thickness field and its variability from 65° N to 81.5° N. Our data reveal a high-frequency interannual variability in mean Arctic ice thickness that is dominated by changes in the amount of summer melt11, rather than by changes in circulation. Our results suggest that a continued increase in melt season length would lead to further thinning of Arctic sea ice.

Spotlight Synthetic Aperture Radar Signal Processing Algorithms

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Bidirectional Reflectance of Flat, Optically Thick Particulate Layers: An Efficient Radiative Transfer Solution and Applications to Snow and Soil Surfaces

Abstract: We describe a simple and highly efficient and accurate radiative transfer technique for computing bidirectional reflectance of a macroscopically flat scattering layer composed of nonabsorbing or weakly absorbing, arbitrarily shaped, randomly oriented and randomly distributed particles. The layer is assumed to be homogeneous and optically semi-infinite, and the bidirectional reflection function (BRF) is found by a simple iterative solution of the Ambartsumian's nonlinear integral equation. As an exact Solution of the radiative transfer equation, the reflection function thus obtained fully obeys the fundamental physical laws of energy conservation and reciprocity. Since this technique bypasses the computation of the internal radiation field, it is by far the fastest numerical approach available and can be used as an ideal input for Monte Carlo procedures calculating BRFs of scattering layers with macroscopically rough surfaces. Although the effects of packing density and coherent backscattering are currently neglected, they can also be incorporated. The FORTRAN implementation of the technique is available on the World Wide Web at http://ww,,v.giss.nasa.gov/-crmim/brf.html and can be applied to a wide range of remote sensing, engineering, and biophysical problems. We also examine the potential effect of ice crystal shape on the bidirectional reflectance of flat snow surfaces and the applicability of the Henyey-Greenstein phase function and the 6-Eddington approximation in calculations for soil surfaces.