Leung Tsang

The Soil Moisture Active Passive (SMAP) Mission

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Councils Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earths land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

Scattering of electromagnetic waves : numerical simulations

Preface. Monte Carlo Simulations of Layered Media. Integral Equation Formulations and Basic Numerical Methods. Scattering and Emission By a Periodic Rough Surface. Random Rough Surface Simulations. Fast Computational Methods for Solving Rough Surface Scattering Problems. Three-Dimensional Wave Scattering from Two-Dimensional Rough Surfaces. Volume Scattering Simulations. Particle Positions for Dense Media Characterizations and Simulations. Simulations of Two-Dimensional Dense Media. Dense Media Models and Three-Dimensional Simulations. Angular Correlation Function and Detection of Buried Object. Multiple Scattering by Cylinders in the Presence of Boundaries. Electromagnetic Waves Scattering By Vegetation. Index.

Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations

This paper presents a model of microwave emissions from rough surfaces. We derive a more complete expression of the single-scattering terms in the integral equation method (IEM) surface scattering model. The complementary components for the scattered fields are rederived, based on the removal of a simplifying assumption in the spectral representation of Greens function. In addition, new but compact expressions for the complementary field coefficients can be obtained after quite lengthy mathematical manipulations. Three-dimensional Monte Carlo simulations of surface emission from Gaussian rough surfaces were used to examine the validity of the model. The results based on the new version (advanced IEM) indicate that significant improvements for emissivity prediction may be obtained for a wide range of roughness scales, in particular in the intermediate roughness regions. It is also shown that the original IEM produces larger errors that lead to tens of Kelvins in brightness temperature, which are unacceptable for passive remote sensing.

A prototype AMSR-E global snow area and snow depth algorithm

A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.

Numerical evaluation of the transient acoustic waveform due to a point source in a fluid-filled borehole

A key measurement employed in oil well wireline logging is the acoustic wave traveltime over a specified formation interval, typically 1 ft. In the traditional measurement, only the compressional head wave is monitored, but for some time it has been obvious that there is significant additional information, such as the shear head wave arrival, in the received waveform. We describe two numerical methods for computing the profile and parameter dependence of the transient waveform based on a model of the acoustic logging problem consisting of a point source on the axis of a fluid‐filled cylindrical borehole. The response to this excitation is determined at a distance from the source, generally on the borehole axis. In the first of the two numerical methods, called “real axis integration”, the complete acoustic waveform is obtained. The second method, called “branch‐cut integration”, evaluates the first compressional and shear‐pseudo‐Rayleigh arrivals individually with much less computation time than the first...

Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow

Dense media radiative transfer (DMRT) equations based on quasicrystalline approximation (QCA) for densely distributed moderate size particles are developed. We first compute the effective propagation constant and coherent transmission into a dense medium on the basis of the generalized Lorentz-Lorenz law and the generalized Ewald-Oseen extinction theorem. The absorption coefficient of the dense media is then calculated. The distorted Born approximation is next applied to a thin layer to determine the bistatic scattering coefficients and the scattering coefficient. The phase matrix in DMRT is then obtained as bistatic scattering coefficient per unit volume. The model is applied to multiple sizes and for sticky particles. Numerical results are illustrated for extinction and brightness temperatures in passive remote sensing using typical parameters in snow terrain. The QCA-based DMRT is also used to compare with satellite Special Sensor Microwave Imager (SSM/I) brightness temperatures for four channels at 19 and 37 GHz with vertical and horizontal polarizations and for two snow seasons. It shows reasonable agreement to snow depth of 1 m.

Backscattering enhancement of random discrete scatterers

A recent laboratory-controlled optical experiment demonstrates that a sharp peak of small but finite angular width is exhibited in backscattering from a random distribution of discrete scatterers. In this paper the phenomenon is explained by using a second-order multiple-scattering theory of discrete particles. The theory gives an angular width of the order of the attenuation rate divided by the wave number and is in agreement with experimental observations. The relations of the present results to past theories on backscattering enhancements are also discussed.

Monte-Carlo simulations of large-scale problems of random rough surface scattering and applications to grazing incidence with the BMIA/canonical grid method

Scattering of a TE incident wave from a perfectly conducting one-dimensional random rough surface is studied with the banded matrix iterative approach/canonical grid (BMIA/CAG) method. The BMIA/CAG is an improvement over the previous BMIA. The key idea of BMIA/CAG is that outside the near-field interaction, the rest of the interactions can be translated to a canonical grid by Taylor series expansion. The use of a flat surface as a canonical grid for a rough surface facilitates the use of the fast Fourier transform for nonnear field interaction. The method can be used for Monte-Carlo simulations of random rough surface problems with a large surface length including all the coherent wave interactions within the entire surface. We illustrate results up to a surface length of 2500 wavelengths with 25000 surface unknowns. The method is also applied to study scattering from random rough surfaces at near-grazing incidence. The numerical examples illustrate the importance of using a large surface length for some backscattering problems. >

Multiple scattering of electromagnetic waves by random distributions of discrete scatterers with coherent potential and quantum mechanical formalism

An experimental observed fact in scattering of electromagnetic waves by dense distribution of discrete scatterers is that the assumption of independent scattering leads to overestimation of scattering effects. To account for this phenomenon in the present paper, the method of coherent potential is applied to the study of multiple scattering of electromagnetic waves by random distribution of discrete scatterers. Comparisons are made with results obtained by using the effective field approximation and the quasicrystalline approximation. Numerical results of the effective dielectric constant and the scattering attenuation rates, as a function of the fractional volume occupied by the scatterers, are illustrated using parameters frequently encountered in the microwave remote sensing of snow and soil moisture. It is shown that the coherent potential method as applied to quasicrystalline approximation is superior to the other approximations in accounting for the overestimation factor.

Modeling Active Microwave Remote Sensing of Snow Using Dense Media Radiative Transfer (DMRT) Theory With Multiple-Scattering Effects

Dense media radiative transfer (DMRT) theory is used to study the multiple-scattering effects in active microwave remote sensing. Simplified DMRT phase matrices are obtained in the 1-2 frame. The simplified expressions facilitate solutions of the DMRT equations and comparisons with other phase matrices. First-order, second-order, and full multiple-scattering solutions of the DMRT equations are obtained. To solve the DMRT equation, we decompose the diffuse intensities into Fourier series in the azimuthal direction. Each harmonic is solved by the eigen-quadrature approach. The model is applied to the active microwave remote sensing of terrestrial snow. Full multiple-scattering effects are important as the optical thickness for snow at frequencies above 10 GHz often exceed unity. The results are illustrated as a function of frequency, incidence angle, and snow depth. The results show that cross polarization for the case of densely packed spheres can be significant and can be merely 6 to 8 dB below copolarization. The magnitudes of the cross polarization are consistent with the experimental observations. The results show that the active 13.5-GHz backscattering coefficients still have significant sensitivity to snow thickness even for snow thickness exceeding 1 m