Development of a winter snow water equivalent algorithm using in situ passive microwave radiometry over snow-covered first-year sea ice

A snow water equivalent (SWE) algorithm has been developed for thin and thick snow using both in situ microwave measurements and snow thermophysical properties, collected over landfast snow covered first-year sea ice during the Canadian Arctic Shelf Exchange Study (CASES) overwintering mission from December 2003 to May 2004. Results showed that the behavior of brightness temperatures (T_bs) in thin snow covers was very different from those in a thick snowpack. Microwave SWE retrievals using the combination of T_b 19 GHz and air temperature (multiple regression) over thick snow are quite accurate, and showed very good agreement with the physical data (R² = 0.94) especially during the cooling period (i.e., from freeze up to the minimum air temperature recorded) where the snow is dry and cold. Thin snow SWE predictions also showed fairly good agreement with field data (R² = 0.70) during the cold season. The differences between retrieved and in situ SWE for both thin and thick snow cover are mainly attributable to the variations in air temperature, snow wetness and spatial heterogeneity in snow thickness.     

Interpretation of Seasat radar-altimeter data over sea ice using near-simultaneous SAR imagery

Abstract

The backscatter properties of Seasat altimeter data in the Beaufort Sea on the 3 October 1978 show distinct zones, which arc interpreted in terms of geophysical characteristics. An overlapping and near-simultaneous synthetic-aperture radar image shows regions of open water, new ice and multi-year sea ice, which correspond to the different zones. It is found that the altimeter signal is sensitive to the ocean-ice boundary and that it indicates the ice type. The pulse-echo waveforms also suggest that several scattering components are present in the returned power over sea ice.     

A unified three-component scattering model for polarimetric coherent target decomposition

In this article, first, the properties of the symmetric scattering space are examined based on the representation of the maximum symmetric scattering component proposed by Touzi. The symmetric scattering unit disc in the complex plane is mapped to a more intuitive unit sphere representation, and a new simpler classification method for symmetric scatterers is proposed based on a simplified symmetric scatterer scattering matrix metric distance. Then, a unified three-component scattering model for polarimetric coherent target decomposition (CTD) is proposed. Pauli decomposition, sphere, dihedral, helix (SDH) decomposition and Cameron decomposition can be expressed as a simplified form of the unified model with some certain restrictions, respectively. In addition, SDH decomposition and Cameron decomposition are proved to be equivalent by verification of restrictions’ consistency. Finally, by the combination of SDH decomposition and symmetric scatterer classification, a scattering matrix classification scheme is proposed to classify the scatterers into 12 scattering types.     

Estimation of snow water equivalent over first-year sea ice using AMSR-E and surface observations

A SWE retrieval algorithm developed in-situ using passive microwave surface based radiometer data is applied to the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E). Snow water equivalent is predicted from two pixels located in Canadian Arctic Shelf Exchange Study (CASES) overwintering study area in Franklin Bay, N.W.T., Canada. Results show that the satellite SWE predictions are statistically valid with measured in-situ snow thickness data in both smooth and rough ice environments where predicted values range from 15 to 25 mm. Stronger correlation between measured and predicted data is found over smooth ice with R² value of 0.75 and 0.73 for both pixels respectively. Furthermore, a qualitative study of sea ice roughness using both passive and active microwave satellite data shows that the two pixels are rougher than the surrounding areas, but the SWE predictions do not seem to be affected significantly.     

基于Yamaguchi分解模型的全极化SAR图像分类

针对利用Yamaguchi分解模型的四个散射分量直接进行类别归属判断精度不高并且所分类别有限的问题,结合模糊C均值的理论,提出了一种基于Yamaguchi分解模型的全极化SAR分类算法,把四个散射分量组成一组归一化的特征矢量,进行FCM聚类分析。并且用日本机载L波段PiSAR数据验证了该算法具有较高的分类精度和较好的视觉效果。     

Removal of azimuth ambiguities and detection of a ship: using polarimetric airborne C-band SAR images

Synthetic aperture radar (SAR) imagery from the sea can contain ships and their ambiguities. The ambiguities are visually identifiable due to their high intensities in the low radar backscatter background of sea environments and can be mistaken as ships, resulting in false alarms in ship detection. Analysing polarimetric characteristics of ships and ambiguities, we found that (a) backscattering from a ship consisted of a mixture of single-bounced, double-bounced and depolarized or diffused scattering types due to its complex physical structure; (b) that only a strong single- or double-bounce scatterer produced ambiguities in azimuth that look like relatively strong double- or single-bounce scatterers, respectively; and (c) that eigenvalues corresponding to the single- or double-bounce scattering mechanisms of the ambiguities were high but the eigenvalue corresponding to the depolarized scattering mechanisms of the ambiguities was low. With these findings, we proposed a ship detection method that applies the eigenvalue to differentiate the ship target and azimuth ambiguities. One set of C-band JPL AIRSAR (Jet Propulsion Laboratory Airborne Synthetic Aperture Radar) polarimetric data from the sea have been chosen to evaluate the method that can effectively delineate ships from their azimuth ambiguities.     

Time series analysis of co-polarization phase difference (PPD) for winter field crops using polarimetric C-band SAR data

The utility of time series polarimetric C-band data for vegetation state monitoring was explored to understand the mechanism of growth and phenology for important winter crops in India. Parameters investigated were HH–VV phase difference (co-polarization phase difference, PPD), amplitude ratio, and polarization indices. Data were acquired during the entire growth phase categorized as early, mid/peak vegetative, and post-vegetative /flowering phase. The trend emerging in this study showed a shift in the phase difference distribution for agricultural areas relating to the growth rate for various crops. The time series data set revealed that the PPD is a function of frequency and was directly affected by crop type (planophile or erectophile), vigour, structure, and crop biophysical parameters, particularly biomass. The behaviour of crop biomass with PPD responded differentially across crop architectures and vigour classes. Co-polarization index was found to be a good measure for discrimination in early growth stages while cross-polarization index suited in advanced vegetative stages where geometrical orientation was uniform. The PPD captured the change in frequency distribution resulting in a peaked distribution at sowing changing to smooth, well-spread frequency distribution as the peak vegetation stage approaches. This histogram nature is observed to be gradual for high-biomass crops and peaked in case of the low-biomass crops. It is indicative of the rate of growth; a low peaked normal curve indicates faster growth rate and resulting in high biomass. The amplitude ratio in the later phase of growth as on the third date is similarly altered as in the VV returns from the crop. Intuition of the rate of growth and plant vigour is obtained from the temporal PPD pattern. The current study shows that while phase differences and amplitude ratio carry little information content on a single resolution cell basis, their spatial distribution over a wider time span can be used to derive quantitative relationships between SAR response and crop condition. The synergy of information involving the above parameters were used to derive useful information on the vegetation.     

C-band radar backscatter of Baltic sea ice: theoretical predictions compared with calibrated SAR measurements

Abstract

Airborne Synthetic Aperture Radar (SAR) data have been analysed together with in situ measurements of sea ice during the Bothnian Experiment in Preparation for ERS-1 (BEPERS) in March 1988. Based on the physical properties of the snow-covered level ice, a scattering model is used to predict the C-band like-polarization backscattering coefficient in an experiment area. Both the average backscattering coefficient and the SAR image texture were found to be in good agreement with the scattering model predictions. The backscatter signature of the level ice was found to be dominated by the ice surface r.m.s. height and autocorrelation function. These parameters were determined from profiles of the ice surface height, which were measured using a laser profiler device with sub-mm accuracy. The present model is expected to be accurate when the backscattering is dominated by scattering from the cm-scale snow or ice surface roughness.     

Terrain topographic inversion using single-pass polarimetric SAR image data

1IntroductionFullypolarimetricSARimagerytechnologyisoneofthemostimportantadvance-mentsforspace-borneremotesensing.Ithasbeenextensivelyappliedtoterrainsurfaceclassification.The22-D(Dimensional)complexscatteringamplitudefunctionsFpq(p,q=v,h),and44-DrealMuellermatrixMij(i,j=1,…,4)canbemeasured[1].Co-polarizedorcross-polarizedbackscatteringsignatureisthefunctionoftheincidencewavewiththeellipticityanglecandorientationangley.Recently,twoflightsofpo-larimetricSARimagedatahavebeenutilizedtogene…     

Analysis of sea ice motion and deformation in the marginal ice zone of the Bering Sea using SAR data

This paper presents a study of sea ice motion and deformation in the marginal ice zone (MIZ) of the Bering Sea in the winter season. Segmentation techniques and statistical methods are applied to high‐resolution synthetic aperture radar (SAR) images to derive ice motion and deformation maps. These techniques involve dynamic local thresholding (DLT), which allows separation of sea ice into different classes of thickness and type. Two ice motion characteristics are observed, one consisting of a translation and a rotation at a scale larger than about 10 km day^?1 and the other consisting of field deformations at a spatial scale of less than about 5 km over a 3‐day period. Sea ice deformation rates are calculated, and the divergence and shear feature of the sea ice in different regions identified. Possible causes, associated with wind, wave, current and internal ice forces, for the sea ice motion and deformation are discussed.     

Fuzzy classification of earth terrain covers using complex polarimetric SAR data

The conventional approach of terrain image classification that assigns a specific class for each pixel is inadequate, because the area covered by each pixel may embrace more than a single class. Fuzzy set theory which has been developed to deal with imprecise information can be incorporate in the analysis for a more appropriate solution to this problem. In the current state of imaging radar technology, polarimetric synthetic aperture radar (SAR) is unique in providing complete polarization information of ground covers for more effective classification than a single polarization radar. In this paper, we use the fuzzy c-means clustering algorithm for unsupervised segmentation of multi-look polarimetric SAR images. A statistical distance measure adopted in this algorithm is derived from the complex Wishart distribution of the complex covariance matrix. In classifying polarimetric SAR imagery, each terrain class is characterized by its own feature covariance matrix. The algorithm searches for cluster centres for each class and generates a fuzzy partition for the whole image. Membership grades obtained for each pixel provide detailed information about spatial terrain variations. Classification of the image is achieved by choosing a defuzzification criterion. When the back-scattering characteristics of two or more classes are not well distinguished from each other, a divisive hierarchical clustering procedure is adopted to locate their respective feature covariance matrices. NASA/JPL AIRSAR data is used to substantiate this fuzzy classification algorithm.     

On the classification of melt season first-year and multi-year sea ice in the Beaufort Sea using Radarsat-2 data

Interpreting satellite microwave sea ice data during the melt season is difficult. Warm temperatures allow for a greater presence of water in the liquid phase at the surface and within the ice, resulting in similar backscattering responses for first-year ice (FYI) and multi-year ice (MYI). Differentiating these ice types is important, especially during summer, in view of the higher presence of seasonal marine traffic, functioning of the ecosystem, and the Inuit use of the marine icescape in summer. In this article, we investigate the similarities between geophysical, thermodynamic, and dielectric characteristics of the late-season MYI and FYI, and discuss how this can lead to a false detection of MYI. The study uses Radarsat-2 data for ice detection during summer. This involves an analysis of co-polarization versus cross-polarization (HH vs. HV), various incident angles (20°, 35°, and 45°), and ice types (FYI vs. MYI). Statistical analyses of the measurements obtained in 2009 identify the difficulty in differentiating ice types during summer. The results show that the physical and electromagnetic properties of the ice surfaces are virtually identical with few differences in the scattering of microwave energy. We conclude with suggestions on how a more effective differentiation of MYI and FYI types in the summer season can be accomplished.     

Discrimination potential of X-band polarimetric SAR data

In anticipation of X-band polarimetric Synthetic Aperture Radar (SAR) data from future sensors, we investigated the potential of X-band fully polarimetric data for discriminating between the principal classes present in a study site near Avignon, France. Decomposition and analysis techniques have been applied to a dataset acquired by the ONERA airborne RAMSES (Radar Aéroporté Multi-Spectral d'Etude des Signatures) SAR. Results show that X-band provides some discrimination capability. The polarimetric parameters, entropy and α-angle, show clearly that these signature classes are grouped into five clusters corresponding to physical scattering characteristics. The introduction of the anisotropy parameter does not increase our ability to distinguish between different classes whose clusters are in the same entropy/α-angle zone. The correlation observed between the radar signal and the surface roughness over bare soils is very low.     

Analysis of coastal ice cover using ERS-I SAR data

An applications demonstration of the use of Synthetic Aperture Radar (SAR) data in an operational selling is being conducted by the National Oceanic and Atmospheric Administration (NOAA) CoastWatch Program. In the development phase of this demonstration, case studies were conducted to assess the utility of SAR data for monitoring coastal ice in the Bering Sea, icebergs from calving glaciers in Prince William Sound, and lake ice in the Great Lakes. ERS-l SAR data was used in these studies. Results showed that depending on size and sea state icebergs could be detected from background and computer enhanced in the imagery, thaI SAR data can supplement and enhance the utility of satellite visible and infrared data sources for coastal ice monitoring, and that Greal Lakes ice cover can be classified by ice type and mapped in the SAR data using image processing techniques. Cloud cover was a common problem. Based on the further development of automated analysis algorithms and the increase in frequency of SAR coverage, the all-weather, day/night viewing capabilities of SAR make it a unique and valuable tool for operational ice detection and monitoring.     

基于物理散射模型的全极化SAR图像增强滤波算法

针对传统的极化SAR滤波方法图像中城镇区域和植被区域地物在滤波中易被混淆, 导致滤波后图像中地物边缘保持效果下降的问题, 提出了一种增强的保持极化散射特性的滤波算法。利用一种增强的四分量极化分解方法获取更加精确的地物散射机制, 并将散射机制信息引入滤波方法中, 使滤波算法中像素的散射机制更精确。增强的四分量极化分解方法引入了极化SAR数据的定向角补偿技术、一种新的体散射模型以及两种散射功率限制条件, 来改进Freeman-Durden分解的结果。理论分析和实验结果表明, 改进后的方法获取了比传统的极化SAR图像滤波算法更加理想的计算结果。     

Sub-pixel evaluation of sea ice roughness using AMSR-E data

The rapidly changing sea ice regime in the Arctic has necessitated an evaluation of sea ice roughness at smaller scales than those provided by satellites. In this article, we evaluate sub-pixel (<5.4 km) sea ice roughness using AMSR-E brightness temperature (T_b) 89 GHz data and in situ physical roughness data acquired using a helicopter-based laser system in the southern Beaufort Sea during April–June of 2008. The analysis shows a statistically significant correlation (r² = 0.61, P-value < 0.05, regression line slope = –79.93) of T_b at horizontal polarization (H-pol) decreasing with increasing root mean square (RMS) heights. These results suggest that 89 H-pol is more sensitive (than vertical polarization (V-pol)) to the changes in physical roughness. The temporal evolution in AMSR-E T_b values at 89 H-pol and 89 V-pol shows a decrease from April to June. We conclude that solely the AMSR-E T_b at 5.4 km is insufficient to fully account for the changes occurring in the dielectric properties and surface roughness of sea ice at sub-pixel level of 1–4 km during April–June.     

A unified framework for crop classification in southern China using fully polarimetric,dual polarimetric,and compact polarimetric SAR data

With the development of synthetic aperture radar (SAR) techniques, various imaging modes that involve single polarimetry, dual polarimetry, full polarimetry (FP), and compact polarimetry (CP) have been proposed and applied to SAR systems. This article attempts to introduce a unified framework for crop classification in southern China using FP, coherent HH/VV, and CP data. By analysing the polarimetric response from different land-cover types (including rice, banana trees, sugarcane, eucalyptus, water, and built-up areas in the experimental site) and by exploring the similarities between data in these three modes, a knowledge-based characteristic space is created and a unified classification framework is presented. Time-series data acquired by TerraSAR-X over the Leizhou Peninsula, southern China, are used in our experiments. The overall classification accuracies for data in the FP and coherent HH/VV modes are approximately 95%, and for data in the CP mode, the accuracy is 91%, which suggest that the proposed classification scheme is effective. Compared with the Wishart Maximum Likelihood (ML) classifier, the proposed method provides approximately 5.64%, 7.30%, and 6.48% higher classification accuracies in the FP, HH/VV, and circular transmit and dual circular receive modes, respectively.     

Measurement of ocean surface slopes and wave spectra using polarimetric SAR image data

Methods have been investigated which use fully polarimetric synthetic aperture radar (SAR) image data to measure ocean slopes and wave spectra. Independent techniques have been developed to measure wave slopes in the SAR azimuth and range directions. The azimuth slope technique, in particular, is a more direct measurement than conventional, intensity based, backscatter cross-section measurements.In the azimuth direction, wave-induced perturbations of the polarimetric orientation angle are used to sense the wave slopes. In the range direction, a new technique involving the alpha parameter from the Cloude-Pottier H-A-? (Entropy, Anisotropy, and (averaged) Alpha) polarimetric scattering decomposition theorem is used to measure slopes. Both measurement types are sensitive to ocean wave slopes and are directional. Taken together, they form a means of using polarimetric SAR (POLSAR) image data to make complete measurements of either ocean wave slopes, or directional wave spectra.These measurements must still contend with fundamental nonlinearities in the SAR image processing (i.e., azimuth direction “velocity bunching”) that are due to wave velocity and acceleration effects.NASA/JPL/AIRSAR L-, and P-band data from California coastal waters were used in the studies. Wave parameters measured using the new methods are compared with those developed using both conventional SAR intensity based methods, and with in situ NOAA National Data Center buoy measurement products.     

简缩极化SAR数据信息提取与应用

全极化(FP)成像模式丰富了合成孔径雷达(SAR)数据的信息量,在地物分类、环境监测、目标探测等领域取得了广泛应用,但是全极化系统受设计和维护复杂度、功率消耗、覆盖范围和数据下传等因素影响,制约了全极化SAR的应用.简缩极化(CP)SAR系统不仅降低了全极化SAR系统的复杂度,还能在一定程度上保持全极化信息,在森林参数反演、地物分类、目标检测等领域已取得了初步的成果.本文简要介绍了简缩极化SAR系统的基本原理,阐述了简缩极化SAR的全极化信息重建及分解的主要方法,并总结其近十年的主要研究成果,最后给出了其发展趋势.     

Comparison of aerial video and Landsat 7 data over ponded sea ice

The development of melt ponds on Arctic sea ice during spring and summer is of great importance to the Arctic climate system as it accelerates the decay of the sea ice and greatly reduces the albedo. Both melt pond development and its spatial distribution are needed to understand the surface energy balance in summer. Previously, a technique was developed for classifying summer sea ice characteristics, including the amount of open water, white (snow-covered) ice, wet ice, and melt ponds using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) spectral information. In this paper, we refine this technique through the use of airborne video data coincident with Landsat ETM+ imagery obtained over Baffin Bay on June 27, 2000. The video images, having a resolution of about 1.5 m at an aircraft altitude of 1.4 km, are classified into open water, ponded or wet ice, and unponded sea ice. Comparison of the video and Landsat imagery shows that many of the melt ponds are too small to cover an entire Landsat pixel (resolution of 30 m) so that the Landsat classification scheme would underestimate melt pond fraction. Thirteen high-resolution video images are classified to develop a method to calculate fractions of open water, ponded or wet ice, and unponded ice from Landsat 7 data. A comparison between these classified video images and Landsat retrievals yields a correlation coefficient of 0.95 with rms errors of less than 9% for the two ice types and 2% for open water. Comparisons of Landsat and video analyses not used in the development of the algorithm yield correlation coefficients of 0.87 for open water, 0.68 for ponded ice, and 0.78 for unponded ice. The rms differences are 10%, 8%, and 11%, respectively.