Modelling of radar backscattering from low-salinity ice with ice ridges

Abstract

Radar backscattering from a snow-free ice surface characterized by an exponential correlation function is studied for conditions typical in the Baltic Sea. The C-band backscattering coefficient from first-year ice is normally found to be characterized primarily by the air-ice surface roughness. However, if the salinity and temperature are low, as in the Baltic Sea, both volume scattering and ice-water surface scattering might contribute. Even more important than scattering from the level ice is scattering from the ice ridges, and a simple approach is taken to characterize the properties of ice ridges and the corresponding scattering.     

EtTect of frost flowers,rough saline snow and slush on the ERS-l SAR backscatter of thin Arctic sea-ice

We present ERS-l Synthetic Aperture Radar (SAR) backscatter measurements and scattering model calculations based on in situ data for thin Arctic sea ice covered with frost flowers, rough saline snow, or slush. The data were acquired in September–October 1991 during the ARCTIC–91 expedition when the air temperature dropped from O°C to –16°C. The ERS–I SAR signatures have a large variability and change rapidly due to environmental conditions. Rough and wet saline snow gave the highest backscattering coefficient of –6dB, whereas smooth slush gave the lowest of –16dB. Newly formed frost flowers gave an intermediate value of –14dB. Application of the backseattering model shows that surface scattering dominates in all cases, except possibly for new frost flowers. Two cases were found where the measured surfaces were too rough for the single scattering model to be used, which calls for multiple scattering effects to be included. Discrepancies between the model and measurements in the remaining cases were in general small or could be explained by inadequate sampling techniques.     

Estimation of the moisture content of bare soil from RADARSAT-1 SAR using simple empirical models

Synthetic Aperture Radar (SAR) provides a remote sensing tool to estimate soil moisture. Mapping surface soil moisture from the grey level of SAR images is a demonstrated procedure, but several factors can interfere with the interpretation and must be taken into account. The most important factors are surface roughness and the radar configuration (frequency, polarization and incidence angle). This Letter evaluates the influence of these variables for estimation of bare soil moisture using RADARSAT-1 SAR data. First, the parameters of two linear backscatter models, the Ji and Champion models (Ji et al . 1995, Champion 1996), were tested and the constants recalculated. rms error based on the backscattering coefficient was reduced from 6.12 and 6.48 dB to 4.28 and 1.68 dB for the Ji and Champion models respectively. Secondly, a new model is proposed which had an rms error of only 1.21 dB. The results showed a marked increase in accuracy compared with the previous models.     

Calculations of radar backscattering coefficient of vegetation-covered soils

A model for simulating the measured radar backscattering coefficient of vegetation-covered soil surfaces is presented in this study. The model consists of two parts: the first is a soil surface model to describe the backscattered radar pulses from a rough soil surface, and the second part takes into account the effect of vegetation cover. The soil surface is characterized by two parameters, the surface height standard deviation σ and the horizontal correlation length l. The effect of vegetation canopy scattering is incorporated into the model by making the radar pulse subject to two-way attenuation and volume scattering when it passes through the vegetation layer. These processes are characterized by the two parameters, the canopy optical thickness τ and the volume scattering factor η. The model results agree well with the measured angular distributions of the radar backscattering coefficient for HH polarization at the 1.6 GHz and 4.75 GHz frequencies over grass-covered fields. These observations were made from an aircraft platform during six flights over a grass watershed in Oklahoma. It was found that the coherent scattering from soil surfaces is very important at angles near nadir, while the vegetation volume scattering is dominant at larger incident angles (> 30°). The results show that least-squares fits to scatterometer data can provide reliable estimates of the surface roughness parameters, particularly the surface height standard deviation σ. The range of values for σ for the six flights is consistent with a 2 or 3 dB uncertainty in the magnitude of the radar response.     

Validation of backscatter models for level and deformed sea-ice in ERS-l SAR images

Backscatter models for level and deformed ice are evaluated based on in situ measurements of Baltic sea-ice and the resuhs are compared with coincident ERS-I Synthetic Aperture Radar (SAR) data. A two-layer scattering model is used for level ice with a dry snow cover. The resuhs show that ice surface scattering dominates in the pack ice, while scattering from the ice-water interface and ice volume scattering are important in the fast ice where the salinity is very low. For deformed ice which consists of large ice blocks, a two-component model is formulated and shown to be independent of the block size distribution. By evaluating the model based on in situ data it is concluded that specular reflections dominate, whereas the small-scale roughness is of less importance. An approach for data inversion is also described, which estimates the ice surface roughness from ERS-l SAR images during dry snow conditions.     

基于高亮特征匹配的双视向SAR图像建筑物高度提取

由于城区场景的复杂性和SAR成像几何畸变的影响,基于单幅SAR图像的建筑物高度提取常常存在很大困难。针对这一问题,利用建筑物目标SAR成像形成的叠掩、二次散射、较强单次散射等散射机制对应的高亮特征非常典型,并且对方向性敏感的特点,提出了一种基于双视向SAR图像高亮特征与几何模型匹配的建筑物高度提取方法。首先分析了建筑物目标的SAR图像散射特征及对雷达视向的敏感性,然后构造了建筑物目标在双视向SAR图像上高亮特征几何模型,然后基于灰度均值、灰度概率分布、边界信息定义匹配函数,并利用多种群遗传算法进行优化求解,最终得到建筑物目标的高度信息。基于模拟和机载SAR图像的试验表明该方法的建筑物高度平均反演误差小于1m,可以有效提高建筑物高度反演的精度。     

A scattering model for perfectly conducting random surfaces I. Model development

Abstract

The standard integral equation for the surface current is solved iterativcly to obtain an estimate of the surface current on a perfectly conducting randomly rough surface. The far-zone scattered fields and the backscattering coefficients for vertical, horizontal and cross-polarizations are then computed using this current estimate. The polarized backscattering coefficients are explicit functions of the surface parameters and reduce to the Kirchhoff solution in the high-frequency region and to the first-order perturbation solution in the low-frequency region. The cross-polarized scattering coefficient reduces to the second-order perturbation result in the low-frequency region and to zero in the high-frequency limit. A comparison is made with scattering measurements taken under laboratory conditions on a random surface with ka equal to 0-44 and kl equal to 3-25 ( l is the correlation length) It is found that better agreement is obtained with the current model than with the first-order perturbation model in predicting polarized scattering. It is also shown that the separation between VV and HH polarizations decreases gradually with frequency and approaches zero in the high-frequency limit     

Classification of low-salinity sea ice types by ranging scatterometer

Abstract

A helicopter-borne 8-channel ranging scatterometer HUTSCAT (Helsinki University of Technology Scatterometer) was used to investigate the backscattering behaviour of low-salinity sea ice at 5-4GHz and 9-8 GHz. The measurements were conducted during the BEPERS-88 Sea Ice Campaign in the Gulf of Bothnia, 6-12 March 1988. The backscattering properties of several sea ice types were examined at the two frequencies, using HH, VV, HV, and VH polarization modes. An incidence angle of 23° off nadir was used in order to investigate the feasibility of the ESA ERS-I (launched in July 1991) SAR for sea ice mapping. The capability of the new scatterometer to identify sea ice types was examined using the following radar output products at 8 channels: (a) the backscattering coefficient and (b) the characteristics of the radar return versus range spectrum (range resolution 65 cm).     

Development of a knowledge based decision tree classifier using hybrid polarimetric SAR observables

ABSTRACT

Fast, efficient and accurate classification of the land cover using Synthetic aperture radar (SAR) observables extracted from hybrid polarimetric SAR data is achieved in this research. The proposed knowledge-based tree classifier utilizes little apriori real-time field survey information along with just four features, namely, backscattering coefficient (σ_rh+σ_rv), scattering mechanism (α), diffuse scattering and odd bounce (surface) scattering feature from m- α decomposition method in a sequential manner. We have exploited the Separability Index (SI) criterion for identifying these 4 features among the available 16 features. The overall accuracy (OA), user accuracy (UA), producer accuracy (PA), kappa coefficient (κ), precision (P), recall (R) and score (F) of the proposed classifier underscores its merits. Further, for the sake of fair comparison with the existing approaches, we have built the layer stacked images using these four features and applied them to the supervised maximum likelihood estimator classifier (MLE) as well as the unsupervised k-means classifier. It is found that the proposed classifier has better performance in terms of OA, UA, PA and κ on different SAR data sets consisting of different areas.     

Simulation of L-band and HH microwave backscattering from coniferous forest stands A comparison with SIR-B data

Abstract

Shuttle Imaging Radar-B (SIR-B) images of coniferous forest stands dominated by Ponderosa pine in the Mt. Shasta region of northern California were used to evaluate a composite L-band HH backscattering model of coniferous forest stands. Eight forest stands were employed to describe the relative trend and distribution of backscattering coefficients. It was found that (1) both SIR-B and simulated backscattering coefficients for the eight stands have similar trends and relations to average tree height and average number of trees per pixel and (2) the dispersion and distribution of simulated backscattering coefficients from each stand broadly matched SIR-B data from the same stand. Although it is difficult to draw any strong conclusions from the comparisons because the experimental data arc limited in both quantity and quality and are also undersampled, the comparisons indicate that a stand-based L-band HH composite model seems promising for explaining backscattering features. The means of the backscattering coefficients are determined by the average tree height and average number of trees per pixel in the stands. The distributions of the backscattering coefficients are modelled through random assignment of tree numbers, heights and spatial distribution within a pixel.     

SAR interferometry over Baltic Sea ice

SAR interferometry (InSAR) offers new interesting possibilities for research in sea ice radar scattering and sea ice mechanics. A case study of this is presented from the Baltic Sea in late March 1992. Interferometric coherence is mainly dependent of the temporal characteristics of the scattering sources in sea ice. Different areas with different scattering properties were examined and the present data indicates that more field data is necessary to fully understand the InSAR coherence over sea ice. However, some interesting features were noted. Over low-salinity ice, backscattering and coherence seems to be related, high backscatter areas are more unstable than low backscatter areas. Over areas with surface roughness scattering, the scattering is relatively stable and also that a snow cover seems to retain the coherence over such areas. Interferometric phase measurements are dependent on small deformations of the ice pack. Fast ice which is (nearly) stationary experiences small discontinuous slips and deformations. Interferometric phase measurements are very sensitive to these slips, displacements and deformations and will provide new insight into the rheology for fast ice and how the fast ice starts to move. How the fast ice starts to move is one of the major problems in sea ice mechanics research and there is not much earlier data on the subject. In the present case, the ice was nearly stationary as the stresses were below the yield limit under the low forcing conditions. Two ice floe compressions have been observed and the strains are believed to be viscous with a viscosity value at approximately 1013-1014kg (ms). Both the interferometric phase and the coherence measurements over ice are believed to be of great value in future backscattering models and sea ice mechanics models.     

An explanation of enhanced radar backscattering from flooded forests

Abstract

A simpie structural backscatter model for a forest stand, suitable for use with L-band HH polarized radar imagery, is used to explain the increased level of backscattering observed from flooded forests. Measurements made of relative levels of backscatter from SIR-B image data of a flooded Australian forest are consistent with an interpretation based upon scattering mechanisms involving both the tree components and the understorey or forest floor. The change in Fresnel power reflection coefficient of the ground with flooding is advanced as the cause of the enhancement in backscattered power levels.     

卷积神经网络和深度置信网络在SAR影像冰水分类的性能评估

目的 海冰分类是海冰监测的主要任务之一。目前基于合成孔径雷达SAR影像的海冰分类方法分为两类：一类是基于海冰物理特性与SAR成像特征等进行分类,这需要一定的专业背景;另一类基于传统的图像特征分类,需要人为设计特征,受限于先验知识。近年来深度学习在图像分类和目标识别方面取得了巨大的成功,为了提高海冰分类精度及海冰分类速度,本文尝试将卷积神经网络（CNN）和深度置信网络（DBN）用于海冰的冰水分类,评估不同类型深度学习模型在SAR影像海冰分类方面的性能及其影响因素。方法 首先根据加拿大海冰服务局（CIS）的冰蛋图构建海冰的冰水数据集;然后设计卷积神经网络和深度置信网络的网络架构;最后评估两种模型在不同训练样本尺寸、不同数据集大小和网络层数、不同冰水比例的测试影像以及不同中值滤波窗口的分类性能。结果 两种模型的总体分类准确率达到93%以上,Kappa系数0.8以上,根据分类结果得到的海冰区域密集度与CIS的冰蛋图海冰密集度数据一致。海冰的训练样本尺寸对分类结果影响显著,而训练集大小以及网络层数的影响较小。在本文的实验条件下,CNN和DBN网络的最佳分类样本尺寸分别是16×16像素和32×32像素。结论 利用CNN和DBN模型对SAR影像海冰冰水分类,并进行性能分析。发现深度学习模型用于SAR影像海冰分类具有潜力,与现有的海冰解译图的制作流程和信息量相比,基于深度学习模型的SAR影像海冰分类可以提供更加详细的海冰地理分布信息,并且减小时间和资源成本。     

Estimation of burnt coal seam thickness in central Borneo using JERS-1 SAR data

We analysed the interaction of microwaves with a burnt coal seam. This analysis approximated a burnt coal seam as a microwave absorber. The impedance of an incident wave with horizontal polarization (transverse magnetic mode) was derived in order to calculate the relationship between burnt coal seam thickness and backscattering coefficient. The result was confirmed by simulating scattering on a burnt coal seam using the Finite Difference Time Domain (FDTD) method. Both were similar. This relationship was used to estimate burnt coal seam thickness in central Borneo using Japanese Earth Resources Satellite (JERS-1) SAR data. Estimation results and ground data were similar.     

Fully polarimetric airborne SAR and ERS SAR observations of snow: implications for selection of ENVISAT ASAR modes

Snow cover has a substantial impact on processes involved in the interaction between atmosphere and surface, and the knowledge of snow parameters is important in both climatology and weather forecasting. With the upcoming launch of Advanced Synthetic Aperture Radar (ASAR) instruments on Envisat, enhanced snow-mapping capabilities are foreseen. In this paper fully polarimetric C- and L-band airborne SAR data, ERS SAR and auxiliary data from various snow conditions in mountainous areas are analysed in order to determine the optimum ASAR modes for snow monitoring. The data used in this study are from the Norwegian part of the snow and ice experiment within the European Multi-sensor Airborne Campaign (EMAC'95) acquired in the Kongsfjellet area, located in Norway, 66°?N, 14°?E. Fully polarimetric C- and L-band SAR data from ElectroMagnetic Institute SAR (EMISAR), an airborne instrument operated by the Danish Center for Remote Sensing (DCR), were acquired in March, May, and July 1995. In addition, several ERS SAR, airborne photos, field and auxiliary data were acquired.

A larger separation between wet snow and bare ground in EMISAR C-VV polarisation data was found at high incidence angle (55°) compared to lower incidence angle (45°). Cross-polarized observations from bare ground, dry and wet snow in the incidence angle range 35° to 65° are below the specified Envisat ASAR noise floor of –20–22 dB. The backscattering angular dependency for wet snow and bare ground derived from EMISAR C-VV and ERS SAR data corresponds well, and agrees to some extent with volume and surface scattering model results. The C-band is more sensitive to variation in snow properties than the L-band.     

混合本征模型的多视SAR影像海冰密度检测

目的 SAR影像中像素光谱测度的空间相关性蕴含着海洋表面和海冰更加丰富的空间特性及其变化信息,因此合理建模这种相关性是高分辨率SAR影像海冰精准解译的关键。提出一种利用随机模型及空间统计学测度刻画海冰空间结构的方法。方法 本文首先,在空间统计学框架下,SAR影像被表示为多值Gamma模型和泊松线Mosaic模型线性加权构建的混合模型,其中多值Gamma模型用于描述海洋表面雷达信号背向散射变化的连续性,而泊松线Mosaic模型则用于表征不同类型海冰表面雷达信号背向散射变化的区域性。利用上述混合模型的一阶、二阶变异函数,建模蕴含在SAR影像中海冰空间结构的变化。结果 对RADARSAT-1影像海冰结构建模并反演其密度。实验区域真实海冰密度分别为20%,80%等,运用本文方法反演所得海冰密度与真实海冰密度误差正负不超过10%。结论 本文提出混合本征模型用以刻画SAR强度影像中海冰像素强度变化的空间关系,能够较好地反演Ungava湾海冰密度分布。为利用遥感影像检测空间机构提供一种全新的方法。     

Investigation of C- and X-band backscattering signatures of Baltic Sea ice

Backscattering signatures of various Baltic Sea ice types and open water leads were measured with the helicopter-borne C- and X-band Helsinki University of Technology scatterometer (HUTSCAT) during six ice research campaigns in 1992–1997. The measurements were conducted at incidence angles of 23° and 45°. The HUTSCAT data were assigned by video imagery into various surface type categories. The ground data provided further classification of the HUTSCAT data into different snow wetness categories (dry, moist and wet snow). Various basic statistical parameters of backscattering signature data were used to study discrimination of open water leads and various ice types. The effect of various physical parameters (e.g. polarization, frequency, snow condition) on the surface type discrimination was investigated. The results from the data analysis can be used to help the development of sea ice classification algorithms for space-borne SAR data (e.g. Radarsat and Envisat). According to the results from the maximum likelihood classification it is not possible to reliably distinguish various surface types in the SAR images only by their backscatter intensity. In general, the best ice type discrimination accuracy is achieved with C-band VH-polarization σ° at an incidence angle of 45°.     

The relation between SAR imagery and regional sea ice ridging characteristics from BEPERS-88

Abstract

In the Bothnian Experiment in Preparation for ERS-1 (BEPERS-88) airborne laser profiles and Synthetic Aperture Radar (SAR) images were obtained simultaneously over the pack ice in the Bay of Bothnia, Baltic Sea. The possibility of mapping ice ridging characteristics using SAR has been analysed. SAR intensity histograms and ridging statistics have been compared in regions with length scales from 3 to 23 km. The measures for the intensity of ridging were taken from the profilometer data as functions of the number of ridges and mean ridge height. The results show that, from SAR intensity distribution, an average of 10 per cent upper tail divided by the overall average is a good predictor for the ridging intensity. This predictor explains 80-90 per cent of the variance.     

A method to estimate forest biomass and its application to monitor Mongolian Taiga using JERS-1 SAR data

This study presents a technique and potential utilization of JERS-1 Synthetic Aperture Radar (SAR) data for the estimation of Taiga species biomass in the Huvsgul Lake basin, Mongolia. In order to develop algorithms for estimating total stand biomass, shapes of the tree trunks were considered. A least-squares method was used to define tree trunk shape coefficients, which were then used to estimate total stand biomass using ground data. L-band data confirmed the backscattering coefficient to be dependent upon not only the quantity of biomass, but also tree parameters. The relationship between backscattering coefficient and forest stand biomass in slope areas of the study area was obtained.     

Backscattering properties of marine phytoplankton Prorocentrum micans

Backscattering coefficients of Prorocentrum micans were determined in the laboratory using the Hydroscat-6 instrument. Ancillary parameters measured included absorption and scattering coefficients, chlorophyll-a concentration, cell size, and cell concentration. Spectral variability was found in the case of the backscattering coefficients, and the maximum backscattering coefficient was obtained at 488 nm, which deviated from the theory because of pigment absorption. We were unable to detect the effect of chlorophyll fluorescence on the shape of the backscattering coefficient spectra at 700 nm. However, the particulate backscattering coefficient was found to be well correlated with chlorophyll-a concentration at the six chosen wavelengths. At the same time, the backscattering ratio was accurately arranged in the range 0.012 to 0.019 at 442 nm and 0.013 to 0.021 at 620 nm. A positive correlation between particle density and backscattering ratio was established with a nonlinear regression model and the correlation coefficient was as high as 0.96 at 442 nm, this providing a good foundation for improving the accuracy of identifying the red tide alga, P. micans, for water colour remote sensing.