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We examine the behavior of the scattered electromagnetic field from random Gaussian power-law surfaces for near grazing incidence.
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We used light detection and ranging (LiDAR) data to calculate roughness patterns (homogeneity, mean-roughness, and entropy) for five lava types at two different resolutions (1.5 and 0.1 m/pixel). We found that end-member types (a a and pahoehoe) are separable (with 95% confidence) at both scales, indicating that roughness patterns are well suited for analyzing types of lava. Intermediate lavas were also explored, and we found that slabby-pahoehoe is separable from the other end-members using 1.5 m/pixel data, but not in the 0.1 m/pixel analysis. This suggests that the conversion from pahoehoe to slabby-pahoehoe is a meter-scale process, and the finer roughness characteristics of pahoehoe, such as ropes and toes, are not significantly affected. Furthermore, we introduce the ratio ENT/HOM (derived from lava roughness) as a proxy for assessing local lava flow rate from topographic data. High entropy and low homogeneity regions correlate with high flow rate while low entropy and high homogeneity regions correlate with low flow rate.We suggest that this relationship is not directional, rather it is apparent through roughness differences of the associated lava type emplaced at the high and low rates, respectively.
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Swept wings and control surfaces are common elements of modern aircraft, and it has been shown both experimentally and theoretically that laminar-to-turbulent transition of the three-dimensional boundary layer that develops over them is highly sensitive to surface roughness. Numerous studies have been conducted on the effect of discrete roughness elements or distributed roughness elements on swept flow transition, however so far limited computational effort has been dedicated to the study of transition over swept wings with randomly distributed micron-sized roughness. In the present work, we set up to reproduce the extensive experimental data base generated by Dagenhart et al for the infinite swept wing NLF(2)-0415. To this purpose, we perform scale-resolving simulations of flow transition over smooth and rough surfaces using a high-order space-time spectral-element Discontinuous-Galerkin solver. Different types of surface roughnesses are implemented by elastically deforming the original mesh. The study shows that the experimental results cannot be accounted for by a perfectly smooth wing and reveals a strong sensitivity of the transition process to the representation of the surface roughness. The crossflow patterns and transition location approach those measured for some of the surface profiles, however a correlation between the wavenumber spectrum of the surface, grid resolution and boundary layer stability is yet to be established.
The paper presents some examples of imaging radar oceanographic observations and discusses physical phenomena on the surface that may cause the radar image. The different ocean scattering theories are briefly discussed, including the tangent plane model, the Bragg-Rice model, and the Rayleigh scattering model. All but one of the images presented were obtained with an L-band HH-polarized radar; they include deep-ocean swells, coastal swells, wave refractions, internal waves, ship wakes, abrupt transitions in open-ocean surface roughness, surface slicks, island wind shadowing, and currents. Analyses are shown to suggest that the primary source of the L-band imagery of ocean surface patterns is the variation of small-scale surface roughness and local tilt angle. It is also noted that surface irregularities behave as isotropic scatterers for a radar wavelength of 25 cm.
Side-Looking Airborne Radar (SLAR) images, acquired by JPL and Strategic Air Command Systems, and visible and near-infrared LANDSAT imagery were applied to studies of the Quaternary alluvial and evaporite deposits in Death Valley, California. Unprocessed radar imagery revealed considerable variation in microwave backscatter, generally correlated with surface roughness. For Death Valley, LANDSAT imagery is of limited value in discriminating the Quaternary units except for alluvial units distinguishable by presence or absence of desert varnish or evaporite units whose extremely rough surfaces are strongly shadowed. In contrast, radar returns are most strongly dependent on surface roughness, a property more strongly correlated with surficial geology than is surface chemistry.
Observations of the distribution of linearly polarized lunar thermal emission were made at a wavelength of 3.1 mm with The University of Texas 4.88 m parabolic reflector (0.042 deg HPBW). A shadow corrected, rough surface, thermal emission model for a homogeneous moon was least-squares-fitted to the polarization data. Results indicate an effective lunar dielectric constant of 1.34 plus or minus 0.04 with surface roughness characterized by a standard deviation of 17 (plus or minus 5) deg for surface slopes with a normal probability density, independent of lunar phase. A comparison of these results with published values at other wavelengths suggests that the effective lunar dielectric constant, as obtained by lunar emission measurements, decreases with decreasing wavelength of observation. This wavelength dependence may be interpreted in terms of an inhomogeneous surface and/or a surface that possesses intermediate scale surface roughness.
This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.
High-resolution Viking Orbiter images contain significant information on Martian surface roughness at 25- to 100-m lateral scales, while earth-based radar observations of Mars are sensitive to roughness at lateral scales of 1 to 30 m or more. High-rms slopes predicted for the Tharsis-Memnonia-Amazonis volcanic plains from extremely weak radar returns are qualitatively confirmed by the Viking image data. Large-scale, curvilinear ridges on lava flows in the Memnonia Fossae region are interpreted as innate flow morphology caused by compressional foldover of moving lava sheets of possible rhyolite-dacite composition. The presence or absence of a recent mantle of fine-grained eolian material on the volcanic surfaces studied was determined by the visibility of fresh impact craters with diameters less than 50 m. Lava flows with surfaces modified by eolian erosion and deposition occur west-northwest of Apollinaris Patera at the border of the cratered equatorial uplands and southern Elysium Planitia. Nearby yardangs, for which radar observations indicate very high-rms slopes, are similar to terrestrial features of similar origin.