Electromagnetic Scattering of Randomly Rough Soil Surfaces Based on Numerical Solutions of Maxwell Equations in Three-Dimensional Simulations Using a Hybrid UV/PBTG/SMCG Method
Abstract:A hybrid UV/PBTG/SMCG method is developed to accelerate the solution of NMM3D for 3-D electromagnetic wave scattering by random rough soil surfaces. It takes only 1.5 min using 16 processors on a cluster of NSF TeraGrid to compute 3-D solution of Maxwell equations of 8 by 8 square wavelengths. With the improved computational efficiency, we computed results using areas up to 32 by 32 square wavelengths. With the larger surface area, we were able to compute cases with larger rms heights up to 8 cm at SMAP radar … Show more
“…Furthermore, ice lenses formed during the ephemeral F/T events in winter can lead to important dielectric discontinuities in the snowpack, causing also possible coherence effects that can have a notable impact on T B [37][38][39]. Further evaluation using multi-layer snow radiative transfer models [40,41] need to better quantify and understand these variations.…”
Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15-50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014-2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (T B ) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, T B was negatively correlated with soil permittivity (ε G ). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects T B . However, because of the effect of snow on L-Band emission, the correlation between T B and ε G decreased with snow accumulation. When compared to satellite measurements, the average T B of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) T B with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., T B increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms.
“…Furthermore, ice lenses formed during the ephemeral F/T events in winter can lead to important dielectric discontinuities in the snowpack, causing also possible coherence effects that can have a notable impact on T B [37][38][39]. Further evaluation using multi-layer snow radiative transfer models [40,41] need to better quantify and understand these variations.…”
Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15-50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014-2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (T B ) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, T B was negatively correlated with soil permittivity (ε G ). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects T B . However, because of the effect of snow on L-Band emission, the correlation between T B and ε G decreased with snow accumulation. When compared to satellite measurements, the average T B of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) T B with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., T B increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms.
“…The convergence of the integration with regard to the number of points used has been checked. To calculate the bistatic scattering coefficient γ we use the distorted Born approximation for the vegetation volume scattering and NMM3D [11] for coherent reflectivity and bistatic rough surface scattering of the soil surface. In the derivations below, the crucial assumptions are: (1) the vegetation scatterers are uniformly distributed in the vegetation layer; (2) each type of vegetation scatterer, such as stalks or leaves, is statistically identical in terms of the size, shape and permittivity; (3) there is no correlation between the scattered fields of different vegetation scatterers, hence the incoherent model can be used; and (4) the first-order scattering contributions are significantly larger than the high-order scattering contributions in the vegetation, such that a single scattering approximation is applicable.…”
Section: Combined Active and Passive Model (Nmm3d-dba)mentioning
confidence: 99%
“…Thus, empirical "best-fit" parameters rather than physical parameters are used in the tau-omega model. For active remote sensing modelling, we previously used the distorted Born approximation (DBA) [10] and the numerical solutions of the Maxwell equations (NMM3D) [11] (this method is called NMM3D-DBA for short), where the coherent reflectivity and rough surface scattering are calculated by NMM3D [12]. This model was used to calculate the V V and HH backscatter at L-band for pasture [13], wheat, winter wheat and canola fields.…”
Section: Introductionmentioning
confidence: 99%
“…The attenuation through the vegetation layer is accounted for by the imaginary part of the effective propagation constant calculated by Foldy's approximation [8]. NMM3D results are based on the Method of Moments (MoM) with the Rao-Wilton-Glisson (RWG) basis function using Gaussian random rough surfaces with exponential correlation functions, which have been shown to agree well with experimental data for various root mean square (RMS ) height values and soil moisture conditions [11]. The total bistatic scattering is expressed as the incoherent sum of three scattering mechanisms: volume scattering, double bounce scattering and surface scattering.…”
“…To extend the validity region, numerical methods including the Method of Moments (MoM), 3D Numerical Method of Maxwell's equations (NMM3D) [22], Advanced Integral Equation Method (AIEM) [23,24], and Extended Boundary Condition Method (EBCM) [25][26][27][28], etc. have been developed.…”
Abstract-Energy conservation is an important consideration in wave scattering and transmission from random rough surfaces and is particularly important in passive microwave remote sensing. In this paper, we study energy conservation in scattering from layered random rough surfaces using the second order small perturbation method (SPM2). SPM2 includes both first order incoherent scattering and a second order correction to the coherent fields. They are combined to compute the total reflected and transmitted powers, as a sum of integrations over wavenumber k x , in which each integration includes the surface power spectra of a rough interface weighted by an emission kernel function (assuming the roughness of each interface is uncorrelated). We calculate the corresponding kernel functions which are the power spectral densities for one-dimensional (1D) surfaces in 2D scattering problems and examine numerical results for the cases of 2 rough interfaces and 51 rough interfaces. Because it is known that the SPM when evaluated to second order conserves energy, and it can be applied to second order for arbitrary surface power spectra, energy conservation can be shown to be satisfied for each value of k x in the kernel functions. The numerical examples show that energy conservation is obeyed for any dielectric contrast, any layer configuration and interface, and arbitrary roughness spectra. The values of reflected or transmitted powers predicted, however, are accurate only to second order in small surface roughness.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.