Variation Of Backscattering Coefficient Research Articles

Backscatter Characteristics Analysis for Flood Mapping Using Multi-Temporal Sentinel-1 Images

Change detection between images of pre-flood and flooding periods is a critical process for flood mapping using satellite images. Flood mapping from SAR images is based on backscattering coefficient differences. The change rules of the backscattering coefficient with different flooding depths of ground objects are essential prior knowledge for flood mapping, while their absence greatly limits the precision. Therefore, minimizing the backscattering coefficient differences caused by non-flood factors is of great significance for improving the accuracy of flood mapping. In this paper, non-flood factor influences, i.e., monthly variations of ground objects and polarization and satellite orbits, on the backscattering coefficient are studied with multi-temporal Sentinel-1 images for five ground objects in Kouzi Village, Shouguang City, Shandong Province, China. Sentinel-1 images in different rainfalls are used to study the variation of the backscattering coefficient with flooding depths. Since it is difficult to measure the flooding depth of historical rainfall events, a hydrological analysis based on the Geographic Information System (GIS) and Remote Sensing (RS) is used to estimate the flooding depth. The results showed that the monthly variations of the maximum backscattering coefficients of farmland and construction and the backscattering coefficient differences caused by the satellite orbit were larger than the minimum backscattering coefficient differences caused by inundation. The flood extraction rules of five objects based on Sentinel-1 were obtained and analyzed, which improved flood extraction knowledge from qualitative to semi-quantitative analysis.

DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA

Abstract. This paper aims to discusses the extraction of urban features from airborne NISAR (NASA-ISRO SAR) data using deep learning algorithm for a part of Ahmedabad City. NISAR data is acquired in two wavelength bands (L and S) in hybrid polarization i.e., RH and RV. This study has used level two data viz., amplitude data. Pre-processing of NISAR data in L and S wavelength bands was carried out by using MIDAS, software developed and provided by the Space Applications Centre. Pre-processing viz., Speckle suppression using different filters in varying window sizes, radiometric and geometric calibration was performed. Variation of backscattering coefficient (Sigma- nought) in different wavelengths and polarizations for different land use features were analysed. NISAR data in conjunction with LISS 4 (5.8 m resolution) data is subjected to different fusion techniques. Qualitative and Quantitative analysis was carried out and Gram Schmidt technique was chosen for further analysis. Segmentation was performed to achieve better analysis of the fused image and the amplitude image. Lastly, a deep learning architecture was developed for the automatic classification of the image, and the Convolution Neural Network model was designed using mobile net and the regularization techniques. Deep learning architecture in conjunction with e-cognition developer was used for extracting urban features.

Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas

Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a better understanding of the temporal decorrelation effects as well as its sources. This paper analyses the temporal decorrelation of the Sentinel-1 C-band backscatter coefficient over burned areas in Mediterranean ecosystems. Several environmental variables influenced the radar scattering such as fire severity, post-fire vegetation recovery, water content, soil moisture, and local slope and aspect were analyzed. The ensemble learning method random forests was employed to estimate the importance of these variables to the decorrelation process by land cover classes. Temporal decorrelation was observed for over 32% of the burned pixels located within the study area. Fire severity, vegetation water content, and soil moisture were the main drivers behind temporal decorrelation processes and are of the utmost importance for areas detected as burned immediately after fire events. When burned areas were detected long after fire (decorrelated areas), due to reduced backscatter coefficient variations between pre- to post-fire acquisitions, water content (soil and vegetation) was the main driver behind the backscatter coefficient changes. Therefore, for efficient synthetic aperture radar (SAR)-based monitoring of burned areas, detection, and mapping algorithms need to account for the interaction between fire impact and soil and vegetation water content.

Modeling L- and X-band backscattering of wheat and tests over fields of Pampas

ABSTRACTA discrete scattering model and a detailed set of ground measurements are used to simulate the backscattering coefficients of wheat fields during the whole growth cycle. Simulations are carried out at L- and X-band, and at HH, VV, and HV polarizations. Wheat fields are located in Pampas (Argentina), and are characterized by low values of plant density. Simulations show that the backscattering coefficient is driven by variations of soil moisture at L-band, particularly for HH polarization, with low vegetation effects. Conversely, the attenuation of vegetation is dominant in producing variations of backscattering coefficients at X-band, particularly for VV polarization. Simulations are compared against experimental data collected over the same Pampas region, using airborne SARAT SAR at L-band and COSMO-SKYMED at X-band. Assuming a surface height standard deviation in a 0.4–0.7 cm range, the simulations generally agree with experimental data, with an RMSE lower than about 2 dB at L-band and X-band, except a limited number of cases. Discrepancies observed in specific conditions are discussed. Overall, the results indicate that a joint use of L- and X-band has a good potential to monitor both soil moisture and vegetation growth.

A remote-sensing-based intensity–duration threshold, Faifa Mountains, Saudi Arabia

Abstract. Construction of intensity–duration (ID) thresholds and early-warning and nowcasting systems for landslides (EWNSLs) are hampered by the paucity of temporal and spatial archival data. This work represents significant steps towards the development of a prototype EWNSL to forecast and nowcast landslides over the Faifa Mountains in the Red Sea Hills. The developed methodologies rely on readily available, temporal, archival Google Earth and Sentinel-1A imagery, precipitation measurements, and limited field data to construct an ID threshold for Faifa. The adopted procedures entail the generation of an ID threshold to identify the intensity and duration of precipitation events that cause landslides in the Faifa Mountains, and the generation of pixel-based ID curves to identify locations where movement is likely to occur. Spectral and morphologic variations in temporal Google Earth imagery following precipitation events were used to identify landslide-producing storms and generate the Faifa ID threshold (I =4.89D−0.65). Backscatter coefficient variations in radar imagery were used to generate pixel-based ID curves and identify locations where mass movement is likely to occur following landslide-producing storms. These methodologies accurately distinguished landslide-producing storms from non-landslide-producing ones and identified the locations of these landslides with an accuracy of 60 %.

Meaningful Object Segmentation From SAR Images via a Multiscale Nonlocal Active Contour Model

The segmentation of synthetic aperture radar (SAR) images is a longstanding yet challenging task, not only because of the presence of speckle, but also due to the variations of surface backscattering properties in the images. Tremendous investigations have been made to eliminate the speckle effects for the segmentation of SAR images, while few work devotes to dealing with the variations of backscattering coefficients in the images. In order to overcome both the two difficulties, this paper presents a novel SAR image segmentation method by exploiting a multi-scale active contour model based on the non-local processing principle. More precisely, we first formulize the SAR segmentation problem with an active contour model by integrating the non-local interactions between pairs of patches inside and outside the segmented regions. Secondly, a multi-scale strategy is proposed to speed up the non-local active contour segmentation procedure and to avoid falling into local minimum for achieving more accurate segmentation results. Experimental results on simulated and real SAR images demonstrate the efficiency and feasibility of the proposed method: it can not only achieve precise segmentations for images with heavy speckles and non-local intensity variations, but also can be used for SAR images from different types of sensors.

Retrieval of Wheat Growth Parameters With Radar Vegetation Indices

The radar vegetation index (RVI) has low sensitivity to changes in environmental conditions and has the potential as a tool to monitor vegetation growth. In this letter, we expand on previous research by investigating the radar response over a wheat canopy. RVI was computed using observations made with a ground-based multifrequency polarimetric scatterometer system over an entire wheat growth cycle. We analyzed the temporal variations of backscattering coefficients for L-, C-, and X-bands; RVI; vegetation water content (VWC); and fresh weight. We found that the L-band RVI was highly correlated with both VWC (r = 0.98) and fresh weight (r = 0.98). Based upon these analyses, linear equations were developed for estimation of VWC (root-mean-square error (RMSE = 0.126 kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> )) and fresh weight (RMSE = 0.12 kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ). In addition, the results of the wheat study were combined with previous investigations with other crops (e.g., rice and soybean). We found that a single linear relationship between L-band RVI and VWC can be used for all crop types (RMSE = 0.47 kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ). These results clearly demonstrate the potential of RVI as a robust method for characterizing vegetation canopies. VWC is a key input requirement for retrieving soil moisture from microwave remote sensing observations. The results of this investigation will be useful for the Soil Moisture Active and Passive mission (2014), which is designed to measure global soil moisture.

Research on backscattering coefficient based on nano-MOSFET noise

In the study of the backscattering coefficient by traditional noise theory, many parameters and quantum mechanical calculations are used, or deviations appear in conclusions due to a lot of assumptions. In the present work, based on the Navid model, the expressions of backscattering coefficient in nano-MOSFET are deduced, and the backscattering coefficient in the linear region and the saturation region are obtained, and gives the back-scattering coefficient measurement method. On this basis, the variations of backscattering coefficient with Channel length, temperature and voltage are analyzed. Furthermore, the variations of backscattering coefficient with voltage in the linear region and the saturation region are verified by experiments and Monte Carlo simulations. Our results are in accordance with other works.

Estimation of Rice Growth Using RADARSTA-2 SAR Images at Seosan Region

Radar remote sensing is appropriate for monitoring rice because the areas where this crop is cultivated are often cloudy and rainy. Especially, Synthetic Aperture Radar (SAR) can acquire remote sensing information with a high temporal resolution in tropical and subtropical regions due to its all-weather capability. This paper analyzes the relationships between backscattering coefficients of rice measured by RADARSAT-2 SAR and growth parameters during a rice growth period. We examined the temporal variations of backscattering coefficients with full polarization. Backscattering coefficients for all polarizations increased until Day Of Year (DOY 222) and then decreased along with Leaf Area Index (LAI), fresh weight, and Vegetation Water Content (VWC). Vertical transmit and Vertical receive polarization (VV)-polarization backscattering coefficients were higher than Horizontal transmit and Horizontal receive polarization (HH)-polarization backscattering coefficients in early rice growth stage and HH-polarization backscattering coefficients were higher than VV-polarization backscattering coefficients after effective tillering stage (DOY 186). Correlation analysis between backscattering coefficients and rice growth parameters revealed that HH-polarization was highly correlated with LAI, fresh weight, and VWC. Based on the observed relationships between backscattering coefficients and variables of cultivation, prediction equations were developed using the HH-polarization backscattering coefficients.

L, C, X-밴드 레이더 산란계 자동측정시스템을 이용한 콩 생육 모니터링

본 연구에서는 다편파 레이더 산란계 자동 측정시스템 이용하여 콩 생육변화를 관측하고 레이더 시스템에서 얻어진 후방산란계수과 콩 생육인자들과의 관계분석을 통하여 콩 생육추정 가능성을 모색하고자 하였다. 2010년도 농촌진흥청 국립식량과학원 연구지역에 다편파 레이더 산란계 관측시스템 (L, C, X-밴드 안테나, 네트워크분석기, RF switch, 입사각 <TEX>$40^{\circ}$</TEX>)을 구축하고 콩 파종시기에서 수확기까지 10분단위로 콩 생육변화를 자동 측정하였다. 모든 안테나 밴드, 편파에서 콩 생육초기 (6월초~7월 중순)에는 VV-편파가 HH-, HV-편파보다 후방산란계수가 높게 나타났고, 그 이후 HH-편파와 다른 편파들 간의 cross-over 현상이 일어났는데 그 시기는 L-밴드가 7월 20일 (DOY 200), C-, X-밴드의 경우에는 7월 30일 (DOY 210)로 밴드에 따라 차이를 보였다. 모든 밴드 및 편파에서 9월 29일 (DOY 271)까지 후방산란계수가 증가하다가 그 이후 감소하였고 특히 종실비대기 (DOY 277, R6) 이후 감소폭이 크게 나타났는데 이 현상은 콩 생육인자 (초장, 엽면적지수, 건물중 등)변화와 일치하였다. 밴드에 따른 후방산란계수와 콩 생육인자들과의 관계를 분석한 결과 L-밴드 HH-편파에서 LAI (<TEX>$r=0.93^{***}$</TEX>), 초장 (<TEX>$r=0.95^{***}$</TEX>), 건물중 (<TEX>$r=0.94^{***}$</TEX>), 꼬투리중 (<TEX>$r=0.92^{***}$</TEX>)등 콩 생육인자들과의 상관계수가 가장 높게 나타났고 이에 비해 X-밴드 편파에서는 콩 생육인자들과의 상관계수가 상대적으로 낮게 나타났다. 후방산란계수 (L-밴드 HH-편파)를 이용하여 콩 생육인자 추정을 위한 회귀식을 작성하였다. Soybean has widely grown for its edible bean which has numerous uses. Microwave remote sensing has a great potential over the conventional remote sensing with the visible and infrared spectra due to its all-weather day-and-night imaging capabilities. In this investigation, a ground-based polarimetric scatterometer operating at multiple frequencies was used to continuously monitor the crop conditions of a soybean field. Polarimetric backscatter data at L, C, and X-bands were acquired every 10 minutes on the microwave observations at various soybean stages. The polarimetric scatterometer consists of a vector network analyzer, a microwave switch, radio frequency cables, power unit and a personal computer. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. The backscattering coefficients were calculated from the measured data at incidence angle <TEX>$40^{\circ}$</TEX> and full polarization (HH, VV, HV, VH) by applying the radar equation. The soybean growth data such as leaf area index (LAI), plant height, fresh and dry weight, vegetation water content and pod weight were measured periodically throughout the growth season. We measured the temporal variations of backscattering coefficients of the soybean crop at L, C, and X-bands during a soybean growth period. In the three bands, VV-polarized backscattering coefficients were higher than HH-polarized backscattering coefficients until mid-June, and thereafter HH-polarized backscattering coefficients were higher than VV-, HV-polarized back scattering coefficients. However, the cross-over stage (HH > VV) was different for each frequency: DOY 200 for L-band and DOY 210 for both C and X-bands. The temporal trend of the backscattering coefficients for all bands agreed with the soybean growth data such as LAI, dry weight and plant height; i.e., increased until about DOY 271 and decreased afterward. We plotted the relationship between the backscattering coefficients with three bands and soybean growth parameters. The growth parameters were highly correlated with HH-polarization at L-band (over r=0.92).

Cross-Imaging Platform Comparison of Ultrasonic Backscatter Coefficient Measurements of Live Rat Tumors

To translate quantitative ultrasound (QUS) from the laboratory into the clinic, it is necessary to demonstrate that the measurements are platform independent. Because the backscatter coefficient (BSC) is the fundamental estimate from which additional QUS estimates are calculated, agreement between BSC results using different systems must be demonstrated. This study was an intercomparison of BSCs from in vivo spontaneous rat mammary tumors acquired by different groups using 3 clinical array systems and a single-element laboratory scanner system. Radio frequency data spanning the 1- to 14-MHz frequency range were acquired in 3 dimensions from all animals using each system. Each group processed their radio frequency data independently, and the resulting BSCs were compared. The rat tumors were diagnosed as either carcinoma or fibroadenoma. Carcinoma BSC results exhibited small variations between the multiple slices acquired with each transducer, with similar slopes of BSC versus frequency for all systems. Somewhat larger variations were observed in fibroadenomas, although BSC variations between slices of the same tumor were of comparable magnitude to variations between transducers and systems. The root mean squared (RMS) errors between different transducers and imaging platforms were highly variable. The lowest RMS errors were observed for the fibroadenomas between 4 and 5 MHz, with an average RMS error of 4 x 10(-5) cm(-1)Sr(-1) and an average BSC value of 7.1 x 10(-4) cm(-1)Sr(-1), or approximately 5% error. The highest errors were observed for the carcinoma between 7 and 8 MHz, with an RMS error of 1.1 x 10(-1) cm(-1)Sr(-1) and an average BSC value of 3.5 x 10(-2) cm(-1)Sr(-1), or approximately 300% error. This technical advance shows the potential for QUS technology to function with different imaging platforms.

Aspect and incidence angle sensitivity in ERS-1 SAR data

Large variations in backscattering coefficients were observed on ERS-1 SAR images acquired from ascending and descending orbits, within 36 hours, over an agricultural area and under stable winter conditions. Three possible causes were investigated: (1) environmental factors; (2) SAR processor instability; and (3) aspect and incidence angle sensitivity. The look direction (aspect angle) accounted for a 1.5dB difference in backscattering coefficients. The remaining variations were related to the incidence angle. A correction model is proposed.

Simple modelling of radar backscattering coefficient over a bare soil: variation with incidence angle, frequency and polarization

Abstract As a basis for inversion algorithms, there is a need for the development of simple backscattering soil models which can account for the variations of incidence angle observed in the same picture or in multiangle systems. A correction factor for the variations of incidence angle is therefore coupled with a classical linear model of the variation of backscattering coefficient with surface soil moisture in a four-parameter model. The correction factor is based on the cosine-type behaviour of the backscattering coefficient as a function of incidence angle, which is observed for rough agricultural surfaces. This simple model is tested on radar measurements performed over a large range of radar configurations. The model is shown to reproduce correctly the observed variations of the radar signal with incidence angle and soil moisture. Its parameters have a physical sense and vary as expected, from literature, with frequency and polarization. When tested on data simulated by the analytical Integral Equat...

A simple Monte Carlo model for electron-probe quantitation

Since the early work of Bishop in the 1960's, many have used Monte Carlo techniques for studying the role of electron scattering in the X-ray production process, but the simulation of individual trajectories has always proved too slow to be of use for online analysis. The paper describes a simple model for calculating the distribution curves of ionisation with depth ϕ(ρz) for a variety of target conditions, which are then characterised by a type of exponential expression capable of much faster computation. This expression is built into a practical correction procedure which can be applied to the analysis of all elements from boron upwards.The Monte Carlo model uses a simple multiple scattering cross-section with 50-step trajectories. This cross-section is adjusted to give the correct variation of backscatter coefficient with target atomic number, as shown in Figure 1, and this is the only physical parameter which it is necessary to fit empirically.

Lidar observation of time and spatial variations of backscattering coefficient near the stratiform cloud base

Investigations of stratiform clouds (SC) conducted by a ground-based triple-beam second harmonic Nd-YAG lidar are presented. By a near-vertical sounding several macrophysical characteristics of SC are studied, namely: cloud base height and its time-and-spatial variations, statistical properties of the backscattering coefficient in the region of the cloud base for various cloud types, the cloud motion velocity behaviour etc. The results obtained from the joint consideration of lidar and conventionally received data are used for the study of cloud-free and cloud-topped atmospheric boundary layers (ABL) and for a short-term forecasting of the SC base height as well.

Lidar Observation of a Mixed-Phase Altostratus Cloud

Measurements by monostatic lidar of linear depolarization ratios and backscatter coefficients in an altostratus cloud revealed a horizontally layered structure. Three different types of layers were observed. The bottom and central layers had depolarization ratios varying from 0.3 to 0.4, which identified them as layers containing mainly ice. The backscatter coefficients were similar to those found in cirrus ice clouds. A central, transient layer had depolarization ratios characteristic of a high-density water cloud, although the total integrated backscatter of 2.3 ± 1.2 was high for this type of cloud. The top layer had a depolarization ratio of 0.2 at the cloud base, decreasing to 0.04 at the cloud center. Backscatter coefficients ranged up to 30 km−1 and the total integrated backscatter was about 7.6 ± 3.8. This value is considerably higher than the range of values predicted for water or cirrus ice clouds and one possible explanation is that specular reflection was occurring from horizontally aligned ice crystal plates. The variation of backscatter coefficient within each layer was rather regular, with a maximum at the center of the layers. The cloud was situated in a stable air stream and its evolution appeared to be slow.

Algebraic expression for the diffuse irradiance reflectivity of water from the two-flow model

A simple expression is obtained for the irradiance reflectance of the ocean based on a two-flow radiative transfer model. This expression is found to reproduce the Monte Carlo calculations to within 7% for scattering albedo values less than 0.8. On the basis of this expression, it is shown that the remotely sensed water color data can be interpreted with the help of iterative optimization techniques, provided that the variations of backscattering coefficient and absorption coefficient with wavelength can be modeled.

Backscattering of 10-100 keV electrons from thick targets

Experimental measurements of backscattering as a function of incident electron beam energy, angle of beam incidence and atomic number of scatterer, together with energy spectra of backscattered electrons, are discussed within the framework of a simple, qualitative theory. A semi-empirical expression for the variation of backscattering coefficient with angle of beam incidence is derived. Spectra integrated over energy and angle of take off also tabulated from experimental measurements for several elements. The use of the experimental data to derive backscattering corrections for X-ray elemental analysis, particularly at non-normal incidence, is briefly described.

Backscattering of 0·5-10 keV electrons from solid targets

Measurements have been made of electron backscattering in a range of incident beam energy from 0·5 to 10 keV. The variation of backscattering coefficient η with energy and angle of incidence, and the energy spectrum of backscattered electrons at incidence and take-off angles of 45°, have been measured. Results from carbon, aluminium-silicon, copper and silver were obtained at a pressure of approximately 10−8 Torr to make the effects of contamination under the electron beam negligible.Various simple theories of the backscattering phenomenon which have been used successfully at higher energies are examined in an attempt to understand the processes producing the observed results. Although the theory of elastic scattering of electrons by atoms at low energies is complicated, it is found that a simple theory based on largeangle elastic collisions may explain the experimentally observed rise in η with fall in beam energy, when an accurate range-energy relationship is used. The variation of η with angle of beam incidence can be explained for low atomic number elements on a diffusion theory, but this explanation does not wholly explain the results for copper at low energies. The energy spectra cannot be predicted by any existing simple theory.