Root Mean Square Height Research Articles

Interaction of a UWB impulse with a layer of sandy soil, having a rough upper boundary

ABSTRACT In this paper, the changes in the character of time shapes, amplitudes, frequency spectra, and time delay of ultra-wideband (UWB) impulses (duration of 0.33 ns) reflected from the layer of wet sand with a rough upper boundary were investigated. It was shown that the attenuation of UWB impulse amplitude, which is reflected from the upper rough boundary of the sand layer, can be described by the model of Fresnel reflection coefficient for a smooth surface using the Gaussian correction factor (FRGC). At this root-mean-square (RMS), height of soil surface roughness was in the range of ~0–21 mm, but the determination coefficient and RMS error (RMSE) were found to be equal to 0.94 and 0.02, respectively. An experiment close to linear increase/decrease in the propagation time of impulses reflected from the upper/lower boundary of the layer, respectively, was found with increasing the RMS heights of upper boundary. It is shown that the propagation time of such impulses can be described by the modified FRGC model with an error from RMSE = 0.09 ns to RMSE = 0.15 ns. The results of these findings are of practical importance for the interpretation of remote sensing data for precise soil moisture measurements from unmanned aerial vehicle platforms using UWB impulses.

Estimation of soil organic carbon by combining hyperspectral and radar remote sensing to reduce coupling effects of soil surface moisture and roughness

Soil organic carbon (SOC) is important in the global carbon cycle. Accurate estimation of SOC content in cultivated land is a prerequisite for evaluating the carbon sequestration potential and quality of soils. However, existing SOC prediction studies based on hyperspectral remote sensing neglect the spectral response of the physical properties of surface soil, leading to inadequate model generalization. With the exponential growth of remote sensing data, the development of pixel-level soil spectral correction methods based on multi-source remote sensing data has become an interesting and challenging topic. This method aims to minimize the effect of soil physical properties on spectra, thus addressing the poor spatiotemporal transferability of SOC prediction models due to uncertain variations in surface soil physical properties. In this study, a soil spectral correction strategy is constructed using satellite hyperspectral image (HSI) and synthetic aperture radar (SAR) images through multi-order polynomial regression and convolutional neural networks. This strategy considers soil physical variables such as soil moisture (SM) content and root mean square height (RMSH) of soil surface roughness. The soil spectral correction model and SOC content prediction model were established using 80 soil samples collected from Site 1. Afterward, the performance and transferability of both models were verified using the remaining 25 samples from Site 1 and 50 samples from Site 2. The results showed that: 1) The effect of SM and RMSH on the soil pixel spectrum can be significantly reduced after correcting HSI using soil spectral correction strategy. The correlation coefficients between the corrected pixel spectrum and the ground-based spectrum increase by over 60 % compared with those between the original spectrum and the ground-based spectrum. 2) Soil spectral correction improves the prediction accuracy and mapping capability of HSI for SOC content, with the highest RP2 of 0.743 and RMSEP of 3.455 g/kg at Site 1. 3) Compared with the original HSI-based SOC prediction model, the soil spectral correction strategy based on multi-order polynomial and convolutional neural network reduced the RMSEP of SOC prediction results at Site 2 by 5.082 g/kg and 5.454 g/kg, and the RP2 increased by 0.390 and 0.409, respectively. 4) When predicting SOC content from raw HIS, SM and RMSH contribute to more than 60 % of the bias, with SM having a larger bias than RMSH. The findings of this study emphasize the influence of soil physical properties on SOC prediction and contribute to the existing research on SOC mapping using HSI and SAR data.

An IGFBM-SAA Fast Algorithm for Solving Electromagnetic Scattering from Layered Media Rough Surfaces

This article proposes a new fast solution algorithm (IGFBM-SAA), which combines the Improved Generalized Forward and Backward Method (IGFBM) with Spectral Acceleration Approach (SAA), which can effectively solve the electromagnetic scattering problem of layered rough surface. In this article, the electric field integral equations (EFIE) for layered rough surfaces is established, and the traditional forward and backward method (FBM) is introduced. Then, based on the traditional FBM algorithm, an Improved Generalized Forward and Backward Method is proposed and, by using the SAA technique in its iterative process, the computation of matrix-vector multiplication is accelerated, thus enabling rapid solution. In the algorithm validation, the same rough surface was calculated using the MoM, FBM, and IGFBM-SAA. The study found that when root mean square (RMS) heights are h1=h2=0.1λ, the convergence accuracy can reach τ=10−7 after 14 iterations. However, as the roughness increases to h1=h2=0.3λ and h1=h2=0.5λ, the convergence accuracy falling to τ=10−5 and τ=10−5, respectively. This indicates that it is necessary to adjust the integration parameters to improve the convergence accuracy. In addition, it was found that when the size of the rough surface is 25.6λ, the computational times for calculations are 91 s (IGFBM-SAA), 197 s (FBM), and 410 s (MoM), respectively. When the size of the rough surface increases to 51.2λ, the computational time differences become more significant, with 236 s, 756 s, and 2547 s being the respective values. This indicates that the proposed algorithm in this article has significant computational speed advantages when dealing with larger rough surfaces. Based on this algorithm, this article studied the electromagnetic scattering characteristics of layered rough surfaces with different parameters (RMS height, dielectric constant, and correlation length), and relevant research results can provide valuable references for areas such as radar target recognition and radar stealth technology, thereby enhancing the accuracy and reliability of radar detection as well as radar stealthperformance.

Measurement and evaluation method of farmland microtopography feature information based on 3D LiDAR and inertial measurement unit

Soil surface roughness (SSR) is an important indicator that characterizes the microtopography feature of farmland after tillage. It has a high practical value for sowing and seedling raising, farmland management, and drainage irrigation in agricultural production. The traditional method often is prone to damage the surface microstructure and results in low efficiency and accuracy. In this study, a new method was proposed to address the limitations of traditional measurement methods of SSR. The proposed measurement and evaluation method of farmland microtopography feature information based on 3D lidar and inertial measurement unit (IMU) could be used to quickly obtain the global point cloud map containing the height data of the test field. Taking three different tillage methods of farmland as the research object, the surface root mean square height (RMSH), correlation length (CL), and their ratio were selected as roughness parameters to explore the anisotropy of microtopography features in different directions. The measurement method was then used to study the effects of sampling processing methods (number, interval, and length) on the measurement accuracy in both OX and OY directions. The results indicate that under the same accuracy requirements, for the 2 × 2 m area, the farmland with different microtopography features needs to be processed with different sample numbers, sample intervals, and sample lengths. The optimal combination of sample parameters for Test field I is sample number of 50, sample interval of 120 mm, and sample interval of 1600 mm, and that in Test field II is sample number of 50, sample interval of 160 mm, and sample interval of 1800 mm. For Test field III, the optimal combination is sample number of 100, sample interval of 40 mm, and sample length of 1200 mm. The experimental results compared with the traditional method illustrate the high accuracy and good feasibility of the proposed method for measuring and evaluating the microtopography feature information of the farmland. The results of the study help to understand the microtopography features and its parameterization of the farmland after tillage, which could further reveal the role and significance of SSR parameters in objectively evaluating farmland tillage quality and optimizing farmland management.

Dynamic Change Patterns of Soil Surface Roughness and Influencing Factors under Different Tillage Conditions in Typical Mollisol Areas of Northeast China

Soil surface roughness is an important factor affecting hydrology and soil erosion processes, and its development is influenced by precipitation, topography, and tillage practices. In this study, the typical mollisol area in northeast China was taken as the research object. Then, the variation in soil surface roughness with time was analyzed under different terrains, as well as different tillage methods, and the effect of the precipitation condition on roughness was also discussed in detail. Through the design of field experiments, the height information of the soil surface was measured using a probe-type roughness plate. Two parameters, the root-mean-square height (RMSH) and the correlation length (CL), were selected to quantitatively characterize the soil surface roughness. In addition, the dynamic change patterns of surface roughness resulting from five tillage methods, including rotary tillage, combined tillage, no tillage, conventional tillage, and reduced tillage, under both sloping and flat land, were compared and analyzed throughout the soybean growing season, under the influence of rainfall. The results show that with the increase in rainfall, the RMSH of the soil surface, under different tillage methods, showed a trend of first decreasing, and then increasing. The results also showed that the RMSHs under rotary tillage, combined tillage, conventional tillage, and reduced tillage in flat land were greater than those in sloping land, and that the CLs of the soil surface under different tillage methods in flat land were smaller than those in sloping land. In addition, the degree of variation in the soil surface roughness was greater in flat land than that in sloping land under all tillage practices, indicating that this study is of great practical importance in the rational selection of tillage methods, and in the scientific quantification of soil erosion, which also show obvious significance for soil and water conservation.

Composite Scattering Evaluation Using Isolate Scattering Data of Target and Rough Surface

In many scenarios, it is easy to obtain the background scattering and the target scattering, respectively. However, it is not always simple to acquire the target's composite scattering, which is crucial for target detection. To surmount this obstacle, we use a convolutional neural network (CNN) inspired by a physical mechanism to first reverse the root mean square height (RMSH) and correlation length (CL). The target's composite model above the rough surface is then created using computer graphics and the Monte Carlo method. Finally, the composite scattering of the integrated model is estimated using by shooting and bouncing ray (SBR) method. The proposed method is tested through simulations and measurements, and the results indicate that the prediction error in the HH- and VV-polarization mode is less than 5%.

Full-Wave Simulation and Scattering Modeling for Terahertz Communications

This paper presents a comprehensive study for analyzing and modeling the scattering phenomenon of terahertz (THz) waves bouncing on rough surfaces. More specifically, a generic parametric methodology to accurately model the rough characteristics focusing on the root-mean-square (RMS) height and correlation length of a surface is proposed. Firstly, a Monte Carlo-based approach is developed for the efficient and accurate software realization of such rough surfaces. Then, the full-wave simulator FEKO is used to simulate at 300 GHz the far-field behavior of scattering waves on 30 distinct surfaces having 6 RMS heights and 5 correlation lengths. By employing the directive scattering (DS) model, the variation of the scattering coefficient, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> , and its equivalent roughness, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha _{R}$</tex-math></inline-formula> , are studied for the scattering amplitude aspect. Furthermore, the statistical characteristics of the phase and polarization of the scattered signals are obtained and compared with those available in existing 3GPP channel model standards which are valid for lower frequency bands. These comparisons show that although the phases follow the normal distribution, as is the case with equivalent 3GPP channel models, the cross-polarization ratios (XPRs) follow the logistic distribution. This is an important difference which could have a great impact on the standardization activities of the current 3GPP-related working groups. Finally, the proposed scattering model at the THz band is realized through an algorithmic implementation. Compared with the classical two-dimensional DS model which characterizes only the amplitude of the scattered signal, the proposed three-dimensional scattering model can be efficiently used to accurately characterize all the important parameters of the scattered signals, namely, amplitude, phase, and polarization at the THz band. Additionally, the proposed scattering model can effectively work in conjunction with ray-tracing (RT) schemes leading to precise scattering and channel modeling for new applications, such as holographic radios and multi-user multiple-input multiple-output (MU-MIMO) systems.

Dynamic development of the Athabasca Valles outflow system from volcanic facies and 15 m scale roughness

Surface roughness properties provide context for geologic terrains, and are commonly partnered with field-based mapping on Earth and satellite-based photogeologic mapping in planetary sciences. In this study, we combine morphology-based facies mapping and perform quantitative roughness analysis and characterization of facies for the Athabasca Valles lava flow-field on Mars to identify flow features, provide insight into eruption conditions, and link roughness patterns throughout the flow to emplacement conditions. The root-mean-square (RMS) height and effective slope were acquired at 15 m wavelength for 14 unique lava facies using statistically derived components from the Shallow Radar (SHARAD) surface echo strength. Quantitative RMS height surface roughness of Athabasca lava features range from 1.09 m to 1.76 m. We show that the RMS height response is generally consistent with facies transitions, confirming the linkage between surficial morphologies and lava flow roughness, including the ability to constrain the relative spatial and temporal evolution of emplacement processes. Roughness patterns and facies localities suggest that the emplacement of Athabasca lava experienced a dynamic progression of local discharge surges and substrate influence on morphology. Given a more complete view of the surface statistical properties, our analyses of nadir-looking radar tracks prove to be a useful tool to distinguish between transitional lava facies.

Ion Beam Figuring System for Synchrotron X-Ray Mirrors Achieving Sub-0.2-µrad and Sub-0.5-nm Root Mean Square

Optics with high-precision height and slope are increasingly desired in numerous industrial fields. For instance, Kirkpatrick–Baez (KB) mirrors play an important role in synchrotron X-ray applications. A KB system is composed of two aspherical, grazing-incidence mirrors used to focus an X-ray beam. The fabrication of KB mirrors is challenging due to the aspherical departure of the mirror surfaces from base geometries and the high-quality requirements for slope and height residuals. In this paper, we present the process of manufacturing an elliptical cylinder KB mirror using our in-house-developed ion beam figuring (IBF) and metrology technologies. First, the key aspects of figuring and finishing processes with IBF are illustrated in detail. The effect of positioning error on the convergence of the residual slope error is highlighted and compensated. Finally, inspection and cross-validation using different metrology instruments are performed and used as the final validation of the mirror. Results confirm that relative to the requested off-axis ellipse, the mirror has achieved 0.15-µrad root mean square (RMS) and 0.36-nm RMS residual slope and height errors, respectively, while maintaining the initial 0.3-nm RMS microroughness.

Regional Variations in the Spreading‐Rate Dependence of Abyssal Hill Roughness as Indicators of Mantle Heterogeneity

AbstractAbyssal hills are potential proxy records of mid‐ocean ridge faulting and volcanism, as well as the spreading rates and mantle properties that influence these processes. Satellite gravity‐based global prediction of abyssal hill root‐mean‐square (RMS) heights could provide such a proxy, but are broadly influenced off‐axis by pelagic sediment cover, which can lower their values by preferentially filling lows. Here I formulate a sediment‐corrected RMS height prediction by estimating an empirical relationship between mean RMS and sediment thickness as a function of spreading rate. I utilize these values to investigate regional variations in the spreading‐rate dependence of mean RMS across eight regions world‐wide, focusing on half spreading rates &lt;40 mm/yr where spreading rate dependence is strongest. I find that regional variations in this relationship are significant, likely indicating heterogeneity in mantle properties between the different regions.

Surface layer response to heterogeneous tree canopy distributions: roughness regime regulates secondary flow polarity

Large-eddy simulation was used to model turbulent atmospheric surface layer (ASL) flow over canopies composed of streamwise-aligned rows of synthetic trees of height, $h$ , and systematically arranged to quantify the response to variable streamwise spacing, $\delta _1$ , and spanwise spacing, $\delta _2$ , between adjacent trees. The response to spanwise and streamwise heterogeneity has, indeed, been the topic of a sustained research effort: the former resulting in formation of Reynolds-averaged counter-rotating secondary cells, the latter associated with the $k$ - and $d$ -type response. No study has addressed the confluence of both, and results herein show secondary flow polarity reversal across ‘critical’ values of $\delta _1$ and $\delta _2$ . For $\delta _2/\delta \lesssim 1$ and $\gtrsim 2$ , where $\delta$ is the flow depth, the counter-rotating secondary cells are aligned such that upwelling and downwelling, respectively, occurs above the elements. The streamwise spacing $\delta _1$ regulates this transition, with secondary cell reversal occurring first for the largest $k$ -type cases, as elevated turbulence production within the canopy necessitates entrainment of fluid from aloft. The results are interpreted through the lens of a benchmark prognostic closure for effective aerodynamic roughness, $z_{0,{Eff.}} = \alpha \sigma _h$ , where $\alpha$ is a proportionality constant and $\sigma _h$ is height root mean square. We report $\alpha \approx 10^{-1}$ , the value reported over many decades for a broad range of rough surfaces, for $k$ -type cases at small $\delta _2$ , whereas the transition to $d$ -type arrangements necessitates larger $\delta _2$ . Though preliminary, results highlight the non-trivial response to variation of streamwise and spanwise spacing.

Millimeter‐ to Decimeter‐Scale Surface Roughness of the Moon at the Chang'e‐4 Exploration Region

AbstractThe surface slope and roughness of the Moon have been investigated extensively over a wide baseline range except millimeter to decimeter scales. In this study, we present for the first time millimeter‐to decimeter‐scale surface slope and roughness of the Moon at China's Chang'e‐4 landing regions (∼20 m across) using the Digital Terrain Model (DTM) with a resolution of 5 mm/pixel. The bidirectional slope at the 7 mm scale can be larger than 40° with a median value of ∼10°. The root‐mean‐square (RMS) height within a window size of 125 mm varies from ∼1 mm to ∼18 mm with a median value of ∼4 mm. Both the bidirectional slope and RMS height show scale‐dependent behaviors and the parameter of scale dependence, the Hurst exponent, is ∼0.6–0.85. We also synthesized the bidirectional slope at baseline from micrometer to kilometer, showing that bidirectional slope decreases from ∼60° at micrometer to ∼1° at a kilometer. At millimeter‐scale, surface roughness is mainly controlled by small impact craters, rocks, and regolith properties. Our roughness results not only bridge the gap in understanding surface roughness from traditional topographic data sets to radar and thermal observations, but also provide valuable information about lunar regolith characteristics, and small‐scale geological processes.

An Improved Model of Height Profile for Drop-On-Demand Print of Ultraviolet Curable Ink

Abstract This paper proposes an improved model of height profile for drop-on-demand printing of ultraviolet curable ink. Unlike previous model, the proposed model propagates volume and covered area based on height difference between adjacent drops. Height profile is then calculated from the propagated volume and area. Measurements of two-drop and three-drop patterns are used to experimentally compute model parameters. The parameters are used to predict and validate height profiles of four and more drops in a straight line. Using the same root-mean-square (RMS) error as benchmark, this model achieves 5.9% RMS height profile error on four-drop lines. This represents more than 60% reduction from graph-based model and an improvement from our previous effort.

“Empirical Prewhitening” Spectral Analysis Detects Periodic but Inconsistent Signals in Abyssal Hill Morphology at the Southern East Pacific Rise

AbstractThe existence, or not, of periodicities in abyssal hill morphology has been vigorously debated in recent publications, and some have hypothesized that such periodicities are evidence of the impact of Milankovitch cycle‐caused sea level fluctuations on the volcanic construction process at mid‐ocean ridges. Periodicities are detected by the presence of spectral peaks that rise significantly above the random variations of sample power spectra associated with an aperiodic, continuous spectrum process, typically modeled as a band‐limited fractal (von Kármán model). Here, I formulate and test a new algorithm to “empirically prewhiten” the sample power spectrum which, without needing to model the continuous spectrum, flattens it to a zero‐mean process. This greatly simplifies definition of the null hypothesis, and additional modeling approximates standard deviation levels that provide a conservative basis for detecting peaks that may be indicative of periodicity. The algorithm is applied to extensive bathymetric data flanking the southern East Pacific Rise. Significant periodicities are detected on many profiles analyzed, but the periods vary widely, and do not cluster at Milankovitch periods. The most substantial harmonic signals detected exhibit periods ∼0.082–0.216 my, with root‐mean square (RMS) heights approximately a quarter to a third of the RMS height for the aperiodic signal. It is hypothesized that the dominant aperiodic component of abyssal hills corresponds to morphology constructed by faults that follow a random distribution governed by scaling laws, whereas longer‐scale periodic signals are associated with crustal thickness variations controlled internally by variations in melt supply.

Meter‐Scale Topographic Roughness of the Moon: The Effect of Small Impact Craters

AbstractHigh‐resolution digital terrain models (DTMs) generated from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Cameras (NACs) provide an opportunity to study surface roughness of the Moon at meter scale. In this study, we mapped and investigated meter‐scale topographic roughness over 462 regions of the Moon using NAC DTMs. Our results show that, at meter to hectometer scales, there are obvious differences in median bidirectional slope, root‐mean‐square (RMS) height, and median absolute slope between maria and highlands. In terms of the median value, ratios of the bidirectional slope, RMS height, and median absolute slope within the maria and highlands are 1: 2.4, 1: 3.0, and 1: 2.7, respectively. However, up to a baseline of ∼0.2 km, no discernible differences in the Hurst exponent and median differential slope exist between the maria and highlands. The Hurst exponent varies from 0.7 to 0.95, with a median value of 0.9 within both the maria and highlands. To identify potential factors affecting meter‐scale roughness of the Moon, we compared the maria with volcanic features on Earth and Mars, as well as the lunar highlands with simulated cratered terrains. We found that the Hurst exponents within the lunar maria are much larger than those of the volcanic features on Earth and Mars, mainly because the lunar maria accumulated more impact craters with diameters smaller than 1 km. The Hurst exponents within the highlands are consistent with those of the simulated cratered terrains, whose surface roughness depends primarily on crater shape, number density, and stratigraphic age. All these results indicate that lunar surface roughness at meter to hectometer scales is mainly controlled by small degraded impact craters.

Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR

ABSTRACT Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer.

Accurate Calculations of Emissivities of Polar Ocean Surfaces Between 0.5 and 2 GHz Using an NIBC/Nystrom/SMCG Method

We use full-wave simulations of rough surfaces to calculate the emissivities of a polar ocean between 0.5 GHz and 2 GHz for applications in ocean salinity. High accuracy is required because the emissivity variations between flat and rough surfaces are at the order of magnitude of $10^{-3}$ . In addition, the changes of emissivity due to salinity change are only about $4.2\times 10^{-4}$ /psu to $2.6\times 10^{-3}$ /psu for 0.5–2 GHz over the polar region. An enhanced method is proposed by combining neighborhood impedance boundary condition (NIBC), Nystrom, and sparse matrix canonical grid (SMCG) to solve the dual surface integral equations (SIEs) using the method of moments (MoM). To simulate large-scale ocean waves using a complete ocean spectrum, the surface root-mean-square (RMS) heights are up to 22.5 cm corresponding to 1.5 times wavelength of 2 GHz. Thus, the surface lengths used are up to 333 wavelengths to account for the large RMS height. Simulation results are illustrated for 0.5–2 GHz. We show that the energy conservations are obeyed to $10^{-4}$ . The effects of roughness and salinity on broadband polar ocean emissivity are studied with the proposed approach. Numerical simulations show that the sensitivity to salinity is still preserved in spite of roughness.

Effect of Bispectrum on Radar Backscattering From Non-Gaussian Sea Surface

The upwind-downwind asymmetry in normalized radar backscattering cross section (NRBCS) from ocean surface is well known; one acceptable and convincing reasoning is explained by the fact that the surface height distribution deviates from Gaussian one, which causes a nonzero skewness function, and consequently affects the radar cross section in up and down wind directions. Specific forms of skewness function, in between Gaussian and exponential, have been proposed in previous studies to account for the upwind/downwind asymmetry in radar backscatter. Attempt is made, through numerical simulation, in this article to examine the impact of these two types of skewness functions on NRBCS. The simulated NBRCSs, with and without skewness contributions, are compared with measured data in upwind and downwind directions at L-, C-, and Ku-bands for different wind speeds. The results indicate that the Gaussian-type skewness function works better to account for the upwind/downwind asymmetry of NBRCS by choosing an appropriate root mean square (RMS) height appearing in skewness function and as the wind speed or radar frequency increases, the RMS height increases but regardless of wind direction and radar incidence angle.

Adaptive Ground Clutter Reduction in Ground-Penetrating Radar Data Based on Principal Component Analysis

Singular value decomposition is an effective way to remove ground clutter in ground-penetrating radar (GPR) applications. The main limitation of this method is the selection of principal components to completely reconstruct the ground clutter or the target. To date, no effective criteria or technology have been developed. To solve this problem, a new method is proposed in this paper. The research and analysis presented herein reveal that the root-mean-square height (RMSH) of the first-arrival curve corresponding to the ground clutter has a well-defined positive relationship with the number of singular values associated with the principal components of the ground clutter. The number of singular values of these principal components ( $N$ ) can be precisely determined based on the ground clutter by a linear function, $N = 0.2634D + 1.3086$ , where D represents the RMSH value. In addition, an algorithm called developed histogram equalization was developed to improve the contrast to highlight the targets in denoized GPR data sets. The proposed strategy of extracting the principal components of the ground clutter and highlighting the contrast between the target signal and environmental reflections was successfully applied to the field GPR data, thus demonstrating the practicality and validity of the proposed approach.

Polarization ratio characteristics of electromagnetic scattering from sea ice in polar areas**Project supported by the National Key Research and Development Program of China (Grant No. 2016YFC1401007), the Global Change Research Program of China (Grant No. 2015CB953901), the National Natural Science Foundation of China (Grant No. 41776181), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No.

In the global climate system, the polar regions are sensitive indicators of climate change, in which sea ice plays an important role. Satellite remote sensing is a significant tool for monitoring sea ice. The use of synthetic aperture radar (SAR) images to distinguish sea ice from sea water is one of the current research hotspots in this topic. To distinguish sea ice from the open sea, the polarization ratio characteristics of sea ice and sea water are studied for L-band and C-band radars, based on an electromagnetic scattering model of sea ice derived from the integral equation method (IEM) and the radiative transfer (RT) model. Numerical experiments are carried out based on the model and the results are given as follows. For L-band, the polarization ratio for sea water depends only on the incident angle, while the polarization ratio for sea ice is related to the incident angle and the ice thickness. For C-band, the sea water polarization ratio is influenced by the incident angle and the root mean square (RMS) height of the sea surface. For C-band, for small to medium incident angles, the polarization ratio for bare sea ice is mainly determined by the incident angle and ice thickness. When the incident angle increases, the RMS height will also affect the polarization ratio for bare sea ice. If snow covers the sea ice, then the polarization ratio for sea ice decreases and is affected by the RMS height of snow surface, snow thickness, volume fraction and the radius of scatterers. The results show that the sea ice and the open sea can be distinguished by using either L-band or C-band radar according to their polarization ratio difference. However, the ability of L-band to make this differentiation is higher than that of C-band.