Improvement of Bare Soil Semi-Empirical Radar Backscattering Models (Oh and Dubois) with SAR Multi-Spectral Satellite Data (X-, C- and L-Bands)

The objective of this study is to improve the performance of semi-empirical radar backscatter models, which are mainly used in microwave remote sensing (Oh 1992, Oh 2004 and Dubois). The study is based on satellite and ground data collected on bare soil surfaces during the Multispectral Crop Monitoring experimental campaign of the CESBIO laboratory in 2010 over an agricultural region in southwestern France. The dataset covers a wide range of soil (viewing top soil moisture, surface roughness and texture) and satellite (at different frequencies: X-, C- and L-bands, and different incidence angles: 24.3° to 53.3°) configurations. The proposed methodology consists in identifying and correcting the residues of the models, depending on the surface properties (roughness, moisture, texture) and/or sensor characteristics (frequency, incidence angle). Finally, one model has been retained for each frequency domain. Results show that the enhancements of the models significantly increase the simulation performances. The coefficient of correlation increases of 23% in mean and the simulation errors (RMSE) are reduced to below 2 dB (at the X and C-bands) and to 1 dB at the L-band, compared to the initial models. At the X- and C-bands, the best performances of the modified models are provided by Dubois, whereas Oh 2004 is more suitable for the L-band (r is equal to 0.69, 0.65 and 0.85). Moreover, the modified models of Oh 1992 and 2004 and Dubois, developed in this study, offer a wider domain of validity than the initial formalism and increase the capabilities of retrieving the backscattering signal in view of applications of such approaches to stronglycontrasted agricultural surface states.

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

A synthetic semi-empirical physical model of secondary electron yield of metals under E-beam irradiation

Calculations of secondary electron yield（SEY） by physical formula can hardly accord with experimental results precisely. Simplified descriptions of internal electron movements in the calculation and complex surface contamination states of real sample result in notable difference between simulations and experiments. In this paper, in order to calculate SEY of metal under complicated surface state accurately, we propose a synthetic semi-empirical physical model. The processes of excitation of internal secondary electron（SE） and movement toward surface can be simulated using this model.This model also takes into account the influences of incident angle and backscattering electrons as well as the surface gas contamination. In order to describe internal electronic states accurately, the penetration coefficient of incident electron is described as a function of material atom number. Directions of internal electrons are set to be uniform in each angle. The distribution of internal SEs is proposed by considering both the integration convergence and the cascade scattering process.In addition, according to the experiment data, relationship among desorption gas quantities, sample ultimate temperature and SEY is established. Comparing with experiment results, this synthetic semi-empirical physical model can describe the SEY of metal better than former formulas, especially in the aspect of surface contaminated states. The proposed synthetic semi-empirical physical model and presented results in this paper can be helpful for further studying SE emission, and offer an available method for estimating and taking advantage of SE emission accurately.

An improved semi-empirical friction model for gas-liquid two-phase flow in horizontal and near horizontal pipes

Pressure drop and liquid hold-up are two very important fluid flow parameters in design and control of multiphase flow pipelines.Friction factors play an important role in the accurate calculation of pressure drop.Various empirical and semi-empirical closure relations exist in the literature to calculate the liquid-wall,gas-wall and interfacial friction in two-phase pipe flow.However most of them are empirical correlations found under special experimental conditions.In this paper by modification of a friction model available in the literature,an improved semiempirical model is proposed.The proposed model is incorporated in the two-fluid correlations under equilibrium conditions and solved.Pressure gradient and velocity profiles are validated against experimental data.Using the improved model,the pressure gradient deviation from experiments diminishes by about 3%;the no-slip condition at the interface is satisfied and the velocity profile is predicted in better agreement with the experimental data.

Adaptive semi-empirical model for non-contact atomic force microscopy

Non-contact atomic force microscope is a powerful tool to investigate the surface topography with atomic resolution.Here we propose a new approach to estimate the interaction between its tips and samples,which combines a semi-empirical model with density functional theory(DFT)calculations.The generated frequency shift images are consistent with the experiment for mapping organic molecules using CuCO,Cu,CuCl,and CuO_(x)tips.This approach achieves accuracy close to DFT calculation with much lower computational cost.

Simulation of a DI Diesel Engine Performance Fuelled on Biodiesel Using a Semi-Empirical 0D Model

Diesel engines have proven over the years important in terms of efficiency and fuel consumption to power generation ratio. Many research works show the potential of biodiesel as a substitute for conventional gasoil. Mainly, previous and recent researches have focused on experimental investigation of diesel engine performance fuelled by biodiesel. Researches on the mathematical description of diesel engine process running on biodiesel are scarce, and mostly about chemical and thermodynamic description of the combustion process of biodiesel rather than performance studies. This work describes a numerical investigation on the performance analysis of a diesel engine fuelled by palm oil biodiesel. The numerical investigation was made using a semi empirical 0D model based on Wiebe’s and Watson’s model which was implemented via the open access numerical calculation software Scilab. The model was validated first by comparing with experimental pressure and performance data of a one cylinder engine at rated speed and secondly by comparing with a six cylinders engine performance data at various crankshaft rotational speeds. Simulations were then made to analyze the engine performance when running on biodiesel. The calculations were made at constant combustion duration and constant coefficient of excess air. Results showed that the model matches the overall experimental data, such as the power output and peak cylinder pressure. The ignition delay was somehow underestimated by the model for the first experiment, which caused a slight gap on in cylinder pressure curve, whereas it predicted the average ignition delay fairly well for the second set of validation. The simulations of engine performance when running on biodiesel confirmed results obtained in previous experimental researches on biodiesel. The model will be further investigated for engine control when shifting to biodiesel fuel.

Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC-Lab), with correlation coefficient (R2) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.

Ultrasonic backscatter characterization of cancellous bone using a general Nakagami statistical model

The goal of this study is to analyze the statistics of the backscatter signal from bovine cancellous bone using a Nakagami model and to evaluate the feasibility of Nakagami-model parameters for cancellous bone characterization. Ultrasonic backscatter measurements were performed on 24 bovine cancellous bone specimens in vitro and the backscatter signals were compensated for the frequency-dependent attenuation prior to the envelope detection. The statistics of the backscatter envelope were modeled using the Nakagami distribution. Our results reveal that the backscatter envelope mainly followed pre-Rayleigh distributions, and the deviations of the backscatter envelope from Rayleigh distribution decreased with increasing bone density. The Nakagami shape parameter(i.e., m) was significantly correlated with bone densities(R = 0.78–0.81, p < 0.001) and trabecular microstructures(|R| = 0.46–0.78, p < 0.05). The scale parameter(i.e.,?) and signal-to-noise ratio(SNR) also yielded significant correlations with bone density and structural features. Multiple linear regressions showed that bone volume fraction(BV/TV) was the main predictor of the Nakagami parameters,and microstructure produced significantly independent contribution to the prediction of Nakagami distribution parameters,explaining an additional 10.2% of the variance at most. The in vitro study showed that statistical parameters derived with Nakagami model might be useful for cancellous bone characterization, and statistical analysis has potential for ultrasonic backscatter bone evaluation.

Modelling of electron energy absorption and backscattering during EBW

A three-dimensional heat flux model for deep-penetrating electron beam welding(EBW)is established to mathematically describe the physical heat generation process during interaction between electrons and the dynamic molten pool free surface.Monte Carlo method is used to determine the electron-target interaction,and random distribution of initial electrons,progressive trajectory tracing and electron backscattering models are used to describe the spatial distribution of electrons absorption.The model is verified in preset keyholes and applied in the simulation on electron beam welding process,and the calculated bead shape shows a good consistency with experimental results.

A backscattering model for a stratified seafloor

In order to predict the bottom backscattering strength more accurately, the stratified structure of the seafloor is considered. The seafloor is viewed as an elastic half-space basement covered by a fluid sediment layer with finite thickness. On the basis of calculating acoustic field in the water, the sediment layer, and the basement, four kinds of scattering mechanisms are taken into account, including roughness scattering from the water-sediment interface, volume scattering from the sediment layer, roughness scattering from the sediment-basement interface,and volume scattering from the basement. Then a backscattering model for a stratified seafloor applying to low frequency（0.1-10 kHz） is established. The simulation results show that the roughness scattering from the sediment-basement interface and the volume scattering from the basement are more prominent at relative low frequency（below 1.0 kHz）. While with the increase of the frequency, the contribution of them to total bottom scattering gradually becomes weak. And the results ultimately approach to the predictions of the high-frequency（10-100 kHz） bottom scattering model. When the sound speed and attenuation of the shear wave in the basement gradually decrease, the prediction of the model tends to that of the full fluid model, which validates the backscattering model for the stratified seafloor in another aspect.

Description of martensitic transformation kinetics in Fe-C-X(X = Ni,Cr,Mn,Si) system by a modified model

Controlling the content of athermal martensite and retained austenite is important to improving the mechanical properties of high-strength steels,but a mechanism for the accurate description of martensitic transformation during the cooling process must be addressed.At present,frequently used semi-empirical kinetics models suffer from huge errors at the beginning of transformation,and most of them fail to exhibit the sigmoidal shape characteristic of transformation curves.To describe the martensitic transformation process accurately,based on the Magee model,we introduced the changes in the nucleation activation energy of martensite with temperature,which led to the varying nucleation rates of this model during martensitic transformation.According to the calculation results,the relative error of the modified model for the martensitic transformation kinetics curves of Fe-C-X(X = Ni,Cr,Mn,Si) alloys reached 9.5% compared with those measured via the thermal expansion method.The relative error was approximately reduced by two-thirds compared with that of the Magee model.The incorporation of nucleation activation energy into the kinetics model contributes to the improvement of its precision.

High-frequency acoustic backscattering characteristics for acoustic detection of the red tide species Akashiwo sanguinea and Alexandrium affine

Harmful algal blooms (HABs), caused by the overgrowth of certain phytoplankton species, have negative effects on marine environments and coastal fisheries. In addition to cell-counting methods using phytoplankton nets, a hydroacoustic technique based on acoustic backscattering has been proposed for the detection of phytoplankton blooms. However, little is known of the acoustic properties of HAB species. In this study, as essential data to support this technique, we measured the acoustic properties of two HAB species, Akashiwo sanguinea and Alexandrium affine, which occur in the South Sea off the coast of Korea. Due to the small size of the target, we used ultrasound for the measurements. Experiments were conducted under laboratory and field conditions. In the laboratory experiment, the acoustic signal received from each species was directly proportional to the cell abundance. We derived a relationship between the cell abundance and acoustic signal received for each species. The measured signals were compared to predictions of a fluid sphere scattering model. When A. sanguinea blooms appeared at an abundance greater than 3 500 cells/mL, the acoustic signals varied with cell abundance, showing a good correlation. These results confirm that acoustic measurements can be used to detect HAB species.

Spectral Analysis and Atmospheric Models of Microflares

By use of the high-resolution spectral data obtained with THEMIS on 2002 September 5, the spectra and characteristics of five well-observed microflares have been analyzed. Our results indicate that some of them are located near the longitudinal magnetic polarity inversion lines. All the microflares are accompanied by mass motions. The most obvious characteristic of the Hα microflare spectra is the emission at the center of both Hα and CaⅡ 8542A lines. For the first time both thermal and non-thermal semi-empirical atmospheric models for the conspicuous and faint microflares are computed. In computing the non-thermal models, we assume that the electron beam resulting from magnetic reconnection is produced in the chromosphere, because it requires lower energies for the injected particles. It is found there is obvious heating in the low chromosphere. The temperature enhancement is about 1000-2200 K in the thermal models. If the non-thermal effects are included, then the required temperature increase can be reduced by 100-150 K. These imply that the Hα microflares can probably be produced by magnetic reconnection in the solar lower atmosphere. The radiative and kinetic energies of the Hα microflares are estimated and the total energy is found to be 10^27 - 4× 10^28 erg.

Sea-surface acoustic backscattering measurement at 6–25 kHz in the Yellow Sea

Sea-surface acoustic backscattering measurements at moderate to high frequencies were performed in the shallow water of the south Yellow Sea, using omnidirectional spherical sources and omnidirectional hydrophones. Sea-surface backscattering data for frequencies in the 6–25 k Hz range and wind speeds of(3.0±0.5)and(4.5±1.0) m/s were obtained from two adjacent experimental sites, respectively. Computation of sea-surface backscattering strength using bistatic transducer is described. Finally, we calculated sea-surface backscattering strengths at grazing angles in the range of 16°–85°. We find that the measured backscattering strengths agree reasonably well with those predicted by using second order small-roughness perturbation approximation method with "PM" roughness spectrum for all frequencies at grazing angles ranged from 40° to 80°. The backscattering strengths varied slightly at grazing angles of 16°–40°, and were much stronger than roughness scattering. It is speculated that scattering from bubbles dominates the backscattering strengths at high wind speeds and small grazing angles. At the same frequencies and moderate to high grazing angles, the results show that the backscattering strengths at a wind speed of(4.5±1.0) m/s were approximately 5 d B higher than those at a wind speed of(3.0±0.5) m/s. However, the discrepancies of backscattering strength at low grazing angles were more than 10 d B. Furthermore the backscattering strengths exhibited no significant frequency dependence at 3 m/s wind speed. At a wind speed of 4.5 m/s, the scattering strengths increased at low grazing angles but decreased at high grazing angles with increasing grazing angle.

基于用户窃听的MU-MISO反向散射通信系统鲁棒资源分配算法

针对反向散射通信系统信道估计不准、信息容易被窃听等问题,该文提出一种基于用户窃听的多用户-多输入单输出(MU-MISO)反向散射通信系统鲁棒资源分配算法,以提高系统传输鲁棒性与信息安全性。首先,考虑基站最大功率、时间分配、信道不确定性、能量收集和保密率等约束,建立一个MU-MISO的反向散射通信系统鲁棒资源分配问题。其次,基于非线性能量收集模型和有界球形信道不确定性模型,利用变量松弛法和S过程将原NP-hard问题转化为确定性问题,随后利用连续凸近似、半正定松弛与块坐标下降法将其转化为凸优化问题求解。仿真结果表明,与传统非鲁棒算法对比,所提算法具有较高的系统容量和较低的中断概率。

环境物联网中的信道估计

应用无源反向散射技术的环境物联网(AIoT,ambient Internet of things)是未来物联网的重要演进方向,当前备受关注。环境物联网实际应用场景中会有相位噪声和杂散引起的强自干扰,这对信道参数估计提出新的挑战。因此,针对两节点AIoT系统提出了一种考虑相位噪声和杂散的有效信道估计迭代算法。该算法基于最小二乘法和复指数基扩展模型(CE-BEM,complex expoential basis expansion model)对信道系数和基变量进行估计,而后利用迭代来提高估计精度。此外,推导了信道估计参数的克拉美罗下界(CRLB,Cramer-Rao lower bound),以评估估计精度的理论极限。最后,通过仿真证明了该估计算法的有效性。

基于空分天线的雷达大气目标探测技术研究

为了拓展新型中间层-平流层-对流层(Mesosphere-Stratosphere-Troposphere, MST)雷达测风工作模式,提高测风数据的时间分辨力,基于全相关分析(Full Correlation Analysis, FCA)方法与大气散射模型,设计了一种应用于MST雷达的空分天线(Spaced-Antenna, SA)测风模式。通过大气散射模型结合雷达实际阵面分布与工作参数产生仿真时域信号,讨论了不同相关函数、相关函数拟合方法以及MST雷达不同天线尺度对雷达测风结果的影响。为验证测风模式的有效性,将信噪比(Signal to Noise Ratio, SNR)不同的测风结果与模型中风场真值进行比较。研究结果表明,新的测风模式仿真计算得到的风速、风向结果精度较高,符合雷达实际探测要求。

盐渍化土壤水分微波遥感反演方法研究与解析

土壤水分是农田粮食产量和质量的关键影响因素,高精度大面积的土壤含水量的反演对于评价不同地区的土壤质量、估产、灌溉等具有重要意义。合成孔径雷达(SAR)是当前进行土壤含水率反演的重要手段之一,但盐渍化农田的土壤含水率SAR反演方法目前还未明确。本文利用Radarsat-2全极化影像结合内蒙古河套灌区农田实地监测数据,构建了盐渍化土壤的含水率的SAR反演模型。首先提取了四种极化方式的后向散射系数,之后通过建立不同后向散射系数组合与土壤水分的线性回归模型的方式,将反演数据与实测数据进行对比,发现HH-VV极化方式取得了较高的精度,最终利用同极化比构建的模型反演了土壤含水率,反演值的均方根误差达到2.23%,其精度能够满足农田土壤含水率反演精度要求。

激光制导武器捕获目标能力研究

为研究激光制导武器在海上、陆上等各种环境下捕获目标的能力,建立激光制导武器飞行轨迹、后向散射、目标漫反射等数学模型,通过比较激光制导武器视场角、后向散射功率密度、目标漫反射功率密度来判别制导武器是否能正常捕获目标。设置相关参数后仿真计算表明在相同的条件下,相同的激光武器在海上相比于在陆上等环境中捕获目标时间更晚,捕获距离更近,后向散射更加严重。一般情况下激光制导武器在本照本投初始4.4s时间阶段内后向散射影响较为严重,通过降低激光照射器的出口能量或拉大激光制导武器中轴线与激光照射器光路距离至250m以上可规避后向散射的影响。