Fusion of satellite active and passive microwave data for sea icetype concentration estimates

Young first-year sea ice is nearly as important as open water in modulating heat flux between the ocean and atmosphere in the Arctic. Just after the onset of freeze-up, first-year ice is in the early stages of growth and will consist of young first-year and thin ice. The distribution of sea ice in this thickness range impacts heat transfer in the Arctic. Therefore, improving the estimates of ice concentrations in this thickness range is significant. The NASA Team Algorithm (NTA) for passive microwave data inaccurately classifies sea ice during the melt and freeze-up seasons because it misclassifies multiyear ice as first-year ice. We developed a hybrid fusion technique for incorporating multiyear ice information derived from synthetic aperture radar (SAR) images into a passive microwave algorithm to improve ice type concentration estimates. First, we classified SAR images using a dynamic thresholding technique and estimated the multiyear ice concentration. Then we used the SAR-derived multiyear ice concentration to constrain the NTA and obtained an improved first-year ice concentration estimate. We computed multiyear and first-year ice concentration estimates over a region in the eastern-central Arctic in which field observations of ice and in situ radar backscatter measurements were performed. The fused estimates of first-year and multiyear ice concentration appear to be more accurate than NTA, based on ice observations that were logged aboard the US Coast Guard Icebreaker Polar Star in the study area during 1991     

Field observations and model calculations of dielectric propertiesof Arctic sea ice in the microwave C-band

The complex dielectric constant of first-year and multiyear sea ice was measured during the Seasonal Ice Monitoring and Modeling (SIMMS) field experiments, conducted in the Arctic in the spring of 1992, 1993, and 1995. The dielectric constant was also computed based on an established dielectric mixing model by using different assumptions about inclusion shape. Computations were based on detailed measurements and observations of ice physical properties and crystalline structure. Comparison between measurements and model results was conducted to identify working models for first-year and multiyear ice. For first-year ice, models that employ the assumption of vertically oriented brine pockets are applicable to columnar ice and those with the assumption of randomly oriented brine pockets are applicable to frazil ice. The validity of the models are established only for ice temperatures less than -8°C. For multiyear ice, there is no need to account for air bubble shape. The coexistence of brine and air inclusions in multiyear pond ice makes it characteristically different from hummock ice. Best results for pond ice were obtained from a simple model that accounts only for volume fractions of inclusions, rather than their shape. Physical parameters that can be retrieved directly from the dielectric constant are salinity of first-year ice at temperatures below -15°C and density of multiyear hummock ice. Detailed measurements of permittivity and loss of first-year and multiyear ice are presented along with some insight into interactions between the dielectric constant and physical parameters     

Arctic Sea Ice Type and Concentration Mapping Using Passive and Active Microwave Sensors

The mapping of ice type concentrations in the Arctic is important for commercial operations and for climate-related research. Algorithms based on moderate-resolution passive microwave sensors for mapping first-year ice and multiyear ice concentrations suffer from a number of known problems. In this paper, it has been shown that QuikSCAT scatterometer data can add complimentary information to that available from passive microwave, which can assist in separating different ice classes. Specifically, we identify a class of ice that exhibits a passive microwave signature which is characteristic of first-year ice, but has a scatterometer signature which is typical of multiyear ice. We track the evolution and distribution of this new ice class throughout the Arctic during the winter season of 2003-2004 and compare the results against the U.S. National Ice Center (NIC) ice charts. It has been found that the new ice class is predominantly multiyear ice and is especially prevalent in the Fram Strait and the high Arctic regions north of the islands Franz Josef Land and Severnaya Zemlya. A simple algorithm has been proposed that enables a passive microwave-based partial ice concentration algorithm (for example, the NT algorithm based on Special Sensor Microwave/Imager data) to be adapted using QuikSCAT scatterometer data, so that the new ice class is corrected from the first-year ice class to the multiyear ice category. The algorithm performance is measured against the NIC ice charts. We provide a discussion regarding the possible physical causes of the effects that have been observed and described     

Classification of pixels in a noisy grayscale image of polar ice

Often, in synthetic aperture radar (SAR) images of polar ice, one encounters shadow-like features across the images. Such features make it difficult to classify pixels into ice and water. Accordingly, it becomes a challenge to determine the true size and boundaries of ice floes in an SAR image of polar ice. We develop a simple statistical procedure which classifies pixels of an image by eliminating the effects of shadow-like features. Methodology developed in this paper is illustrated using some noisy SAR images of ice floes in the Arctic sea.     

Sea ice classification using SAR backscatter statistics

Sea ice classification accuracy using standard statistics and higher order texture statistics generated from grey-level co-occurrence (GLC) matrices were compared for synthetic aperture radar (SAR) data collected during the Marginal Ice Zone Experiment (MIZEX) in April 1987. Standard stepwise discriminate analysis was used to identify the statistics modes useful for discrimination. Range was the most effective statistic, correctly classifying the ice types 75% of the time. Overall, the standard statistics (mean, variance, range, etc.) outperformed the texture statistics (87% accuracy vs. 75% accuracy). Given the added difficulty and computational cost of generating texture statistics, this result suggests that standard statistics should be used for sea ice classification. Odden and multiyear ice categories were the most difficult to statistically separate for these data     

风云三号卫星MERSI影像提取北极海冰参数的方法

风云三号卫星MERSI影像的空间分辨率达到250 m,不仅可以长期宏观监测海冰整体分布变化,而且能够观测分析破碎冰块的面积、圆度等细节形态特征.从海冰光谱特征和冰块灰度分布特征出发,给出MERSI影像提取海冰整体分布参数和冰块形态参数方法,包括海冰识别、冰块分离提取以及海冰参数提取,利用这些方法成功提取了2014年夏季弗拉姆海峡海冰边缘区的海冰细节信息.给出的方法可为研究分析北极海冰变化及海气相互作用提供方法支持和信息保障.     

A SAR for Real-Time Ice Reconnaissance

Prior research has shown that open water first-year ice and multi-year ice can be distinguished on an image obtained by a Synthetic-aperture radar (SAR). A small lightweight SAR, STAR-1, has been built that employs this capability for support of engineering operations for oil exploration in the Arctic. The SAR is a fully focused side-looking system capable of mapping either side of the aircraft. It has two modes, wide swath, which covers 45 km, and high resolution, which covers 22 km. The azimuth resolution is 6 m for both modes, with a range resolution of 12 m in the wide swath and 6 meters in the high resolution mode. The instrument is installed in a Cessna 441 Conquest capable of flying at high altitudes with minimum fuel consumption. An image is produced in real time by a digital image formation processing system aboard the aircraft. This image is transmitted to a ground station via a data link where a hardcopy is formed on heat-developed film.     

Analysis of glaciers and geomorphology on Svalbard usingmultitemporal ERS-1 SAR images

The all-weather capability makes synthetic aperture radar (SAR) interesting with respect to glaciological studies in remote Arctic areas. The aim of this project was to investigate how the European Remote Sensing Satellite (ERS) SAR backscatter from certain glaciological and geomorphological structures varied with time and find out at which time of the year a SAR acquisition will give the best result when observing certain features. Five ERS-1 SAR images, one from the winter and four from the summer, were acquired over the northwestern part of Svalbard, Norway. Ground measurements and observations were made at the same time as one of the summer SAR acquisitions. The ground data as well as meteorological recordings were used to analyze the SAR backscatter changes in the multitemporal data set. A zonation on the glaciers representing snow, firn/superimposed ice, and glacier ice was detected in the winter image only. The equilibrium line could also be derived from this SAR image. Information on drainage patterns, distribution of wet and dry snow areas, and the occurrence of crevasses was derived from the summer images. Changes in snow cover distribution was identified in the summer images. The form and position of ice-cored moraines, fossil beach ridges, and river channels were identified in all images. Early summer images showed the highest potential for identification of landforms. Small-scale landforms, such as patterned ground and tundra polygons, could not be identified     

Surface roughness characterizations of sea ice and ice sheets: case studies with MISR data

This work is an examination of potential uses of multiangular remote sensing imagery for mapping and characterizing sea ice and ice sheet surfaces based on surface roughness properties. We use data from the Multi-angle Imaging SpectroRadiometer (MISR) to demonstrate that ice sheet and sea ice surfaces have characteristic angular signatures and that these angular signatures may be used in much the same way as spectral signatures are used in multispectral classification. Three case studies are examined: sea ice in the Beaufort Sea off the north coast of Alaska, the Jakobshavn Glacier on the western edge of the Greenland ice sheet, and a region in Antarctica south of McMurdo station containing glaciers and blue-ice areas. The MISR sea ice image appears to delineate different first-year ice types and, to some extent, the transition from first-year to multiyear ice. The MISR image shows good agreement with sea ice types that are evident in concurrent synthetic aperture radar (SAR) imagery and ice analysis charts from the National Ice Center. Over the Jakobshavn Glacier, surface roughness data from airborne laser altimeter transects correlate well with MISR-derived estimates of surface roughness. In Antarctica, ablation-related blue-ice areas, which are difficult to distinguish from bare ice exposed by crevasses, are easily detected using multiangular data.     

Application of neural networks for sea ice classification inpolarimetric SAR images

Several automatic methods have been developed to classify sea ice types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are generally grouped into supervised and unsupervised approaches. In previous work, supervised methods have been shown to yield higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new classification technique is applied to determine sea ice types in polarimetric and multifrequency SAR images, utilizing an unsupervised neural network to provide automatic classification, and employing an iterative algorithm to improve the performance. The learning vector quantization (LVQ) is first applied to the unsupervised classification of SAR images, and the results are compared with those of a conventional technique, the migrating means method. Results show that LVQ outperforms the migrating means method, but performance is still poor. An iterative algorithm is then applied where the SAR image is reclassified using the maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy. The new algorithm successfully identifies first-year and multiyear sea ice regions in the images at three frequencies. The results show that L- and P-band images have similar characteristics, while the C-band image is substantially different. Classification based on single features is also carried out using LVQ and the iterative ML method. It is found that the fully polarimetric classification provides a higher accuracy than those based on a single feature. The significance of multilook classification is demonstrated by comparing the results obtained using four-look and single-look classifications     

Focused synthetic aperture radar processing of ice-sounder datacollected over the Greenland ice sheet

The authors developed a synthetic aperture radar (SAR) processing algorithm for airborne/spaceborne ice-sounding radar systems and applied it to data collected in Greenland. By using focused SAR (phase-corrected coherent averaging), they improved along-track resolution by a factor of four and provided a 6-dB processing gain over unfocused SAR (coherent averaging without phase correction) based on a point-target analysis for a Greenland ice-sounding data set. Also, They demonstrated that the focused-SAR processing reduced clutter and enabled them to identify bedrock-interface returns buried in clutter. Using focused-SAR technique, they processed data collected over a key 360-km-long portion of the 2000-m contour line of southwest Greenland. To the best of their knowledge, these are the first high-quality radar ice thickness measurements over this key location. Moreover, these ice-thickness measurements have been used for improving mass-balance estimates of the Greenland ice sheet     

Maximum likelihood estimation of K distribution parameters for SARdata

The K distribution has proven to be a promising and useful model for backscattering statistics in synthetic aperture radar (SAR) imagery. However, most studies to date have relied on a method of moments technique involving second and fourth moments to estimate the parameters of the K distribution. The variance of these parameter estimates is large in cases where the sample size is small and/or the true distribution of backscattered amplitude is highly non-Rayleigh. The present authors apply a maximum likelihood estimation method directly to the K distribution. They consider the situation for single-look SAR data as well as a simplified model for multilook data. They investigate the accuracy and uncertainties in maximum likelihood parameter estimates as functions of sample size and the parameters themselves. They also compare their results with those from a new method given by Raghavan (1991) and from a nonstandard method of moments technique; maximum likelihood parameter estimates prove to be at least as accurate as those from the other estimators in all cases tested, and are more accurate in most cases. Finally, they compare the simplified multilook model with nominally four-look SAR data acquired by the Jet Propulsion Laboratory AIRSAR over sea ice in the Beaufort Sea during March 1988. They find that the model fits data from both first-year and multiyear ice well and that backscattering statistics from each ice type are moderately non-Rayleigh. They note that the distributions for the data set differ too little between ice types to allow discrimination based on differing distribution parameters     

Sea ice monitoring by L-band SAR: an assessment based on literature and comparisons of JERS-1 and ERS-1 imagery

Spaceborne single-polarization C-band synthetic aperture radar (SAR) imagery is widely used to gather information about the state of the sea ice cover in the polar regions. C-band is regarded as a reasonable choice for all-season monitoring capabilities. For specific mapping tasks, however, other frequency bands can be more suitable. In the first part of this paper, the summary of a literature study dealing with the utilization of L-band SAR imagery for sea ice monitoring is presented. Investigations reveal that if deformation features such as ice ridges, rubble fields, and brash ice are to be mapped, L-band radar is superior in a number of cases. The second part of this paper addresses the comparison of JERS-1 and ERS-1 SAR images that were acquired over sea ice east of Svalbard and along the east coast of Greenland. The effects of the different frequencies, polarizations, and incidence angles of the two SAR systems are discussed. It is demonstrated that the images of both sensors complement one another in the analysis of ice conditions, resulting in a more detailed view of the sea ice cover state.     

Arctic ice type identification by radar

The pack ice in the Arctic Ocean was the subject of a special remote sensing mission conducted jointly by the National Aeronautics and Space Administration, the Navy Oceanographic Office, the U. S. Army Cold Regions Research and Engineering Laboratory, the Arctic Institute of North America, and the University of Kansas. One of the most significant results of the experiment was verification of the ice type identification potential of a 2.25-cm-wavelength radar scatterometer. The results of the radar experiment are presented and the data are analyzed to determine the characteristics of radar backscatter from various Arctic ice types. A quantitative analysis of the data indicates that identifiable radar return "signatures" are obtained for each of several specific sea ice types. A qualitative analysis based on the Kirchhoff-Huygens principle suggests that a categorizing factor, denoted as the surface roughness factor, can be assigned to each of several ice types.     

Synergy of active and passive satellite microwave data for the study of first-year sea ice in the Caspian and Aral seas

The paper discusses application of active and passive microwave data for assessment of time and space variations of first-year ice cover. The Caspian and Aral seas are chosen as main study areas. The Caspian Sea evolution is primarily climate driven, while for the Aral Sea there is a mix of anthropic and climate factors. We analyze ice cover conditions using a novel method that combines active and passive satellite measurements for ice discrimination. This method uses the synergy of simultaneous data from active (radar altimeter) and passive (radiometer) microwave instruments onboard the TOPEX/Poseidon (T/P) satellite, launched in 1992. The benefits, drawbacks, and potential of ice cover studies using the proposed method are discussed. We analyze in detail how this method is influenced by the difference in footprints of the T/P sensors and by the radiometric properties of ice and snow at different stages of ice cover evolution. In order to link the T/P-derived results to historical observations that end in the mid-1980s, long time series of passive microwave data from SMMR and SSM/I sensors have also been analyzed. Satellite time series of ice cover extent and duration of ice period have been obtained for the Caspian and Aral seas since 1978. A good agreement is obtained between historical and satellite data, with significant spatial and temporal variability of ice conditions. There is a marked decrease of both duration of ice season and ice extent during the winters 1998/1999-2001/2002. These satellite-derived time series of sea ice parameters are very valuable in view of the heterogeneous and mostly unpublished data on ice conditions over the Caspian and Aral seas since the mid-1980s.     

Polarimetric Characteristics of sea ice in the sea of Okhotsk observed by airborne L-band SAR

The Phased-Array L-Band SAR (PALSAR) aboard the Advanced Land Observing Satellite (ALOS) is capable of globally acquiring fully polarimetric data. In order to confirm the ability of L-band polarimetric synthetic aperture radar (SAR) to investigate sea ice before the ALOS launch, we conducted a field experiment using an airborne Polarimetric and Interferometric SAR (Pi-SAR) in the Sea of Okhotsk in 1999. This paper presents the analyzed results of data acquired in that experiment. The extracted polarimetric parameters of several ice types suggested that polarimetric coherences and phase differences between right-right (RR) and left-left (LL) are good candidates for discriminating ice types. The polarimetric anisotropy as well as the beta angle of the first eigenvector calculated in the polarimetric decomposition procedure are alternative parameters that are sensitive to ice type differences. Due to the low depolarization characteristics of open water, it could be discriminated from sea ice by scattering entropy in all incidence angle ranges. From the relation between ice thickness and the polarimetric parameters, we found that backscattering coefficients and vertical (VV) to horizontal (HH) backscattering ratio are highly correlated with ice thickness. Since the ratio is sensitive to ice surface dielectric constants, a simple simulation using the integral equation method surface model was conducted by using the physical parameters of typical sea ice. A two-dimensional ice thickness map was derived from an empirical relation between the VV-to-HH backscattering ratio and ice thickness.     

机翼铝蒙皮积冰及冰厚的近红外多光谱检测

提出一种机翼铝蒙皮积冰及冰厚的近红外多光谱检测方法,并根据理论模型进行实验验证。首先,根据冰和水在近红外光谱下的光谱曲线不同,取1.16μm和1.26μm两个通道的图像的灰度值,求得对比度C值;然后,通过实验获得C的阈值Cth。CCth时,则认为是冰,CCth时,则认为没有冰,以此法来识别铝蒙皮表面上积冰的位置。本文通过实验证明了冰厚与对比度C值存在线性关系。根据Cth和线性比例系数k两个参数,由对比度C值的大小,可以获得冰的厚度值。     

A microwave technique for mapping ice temperature in the Arcticseasonal sea ice zone

A technique for deriving ice temperature in the Arctic seasonal sea ice zone from passive microwave radiances has been developed. The algorithm operates on brightness temperatures derived from the Special Sensor Microwave/Imager (SSM/I) and uses ice concentration and type from a previously developed thin ice algorithm to estimate the surface emissivity. Comparisons of the microwave derived temperatures with estimates derived from infrared imagery of the Bering Strait yield a correlation coefficient of 0.93 and an RMS difference of 2.1 K when coastal and cloud contaminated pixels are removed. SSM/I temperatures were also compared with a time series of air temperature observation from Gambell on St. Lawrence Island and from Point Barrow, AK weather stations. These comparisons indicate that the relationship between the air temperature and the ice temperature depends on ice type     

Evaluation of late summer passive microwave Arctic sea iceretrievals

The melt period of the Arctic sea ice cover is of particular interest in studies of climate change due to the albedo feedback mechanisms associated with meltponds and openings in the ice pack. The traditionally used satellite passive microwave sea ice concentration algorithms have deficiencies during the summer months due to the period's highly variable surface properties. A newly developed ice concentration algorithm overcomes some of these deficiencies. It corrects for low ice concentration biases caused by surface effects through the use of 85 GHz data in addition to the commonly used 19 and 37 GHz data and, thus, the definition of an additional ice type representing layering and inhomogeneities in the snow layer. This new algorithm will be the standard algorithm for Arctic sea ice concentration retrievals with the EOS Aqua advanced microwave scanning radiometer (AMSR-E) instrument. In this paper, we evaluate the performance of this algorithm for the summer period of 1996 using data from the special sensor microwave imager (SSM/I) which has frequencies similar to the AMSR instrument. The temporal evolution of summertime passive microwave sea ice signatures are investigated and sea ice concentration retrievals from the standard NASA team and the new algorithm are compared. The results show that the introduction of the additional sea ice type in the new algorithm leads to improved summertime sea ice concentrations. The SSM/I sea ice retrievals are validated using SAR-derived ice concentrations that have been convolved with the SSM/I antenna pattern to ensure an appropriate comparison. For the marginal ice zone, with ice concentrations ranging from 40% to 100%, the correlation coefficient of SAR and SSM/I retrievals is 0.66 with a bias of 5% toward higher SAR ice concentrations. For the central Arctic, where ice concentrations varied between 60% and 100%, the correlation coefficient is 0.87 with a negligible bias     

SAR image statistics related to atmospheric drag over sea ice

The possibility of using synthetic aperture radar (SAR) data to distinguish sea-ice regions with different atmospheric drag is explored. Both the amplitude of the radar return and statistics derived from SAR image data are examined. Roughness statistics data from several pack-ice areas are used in a backscatter model to predict the return from surfaces with measured drag coefficients. The results suggest that the scattering coefficient for typical radar wavelengths is insensitive to the roughness elements responsible for the observed drag coefficient variations over pack ice free of major ridges. For marginal ice zones, where ice concentration and floe deformation contribute to atmospheric drag, a simple model for the atmospheric boundary layer is used to provide qualitative relationships between drag coefficient and regional ice properties (ice concentration, floe size distribution, floe edge density) derivable from SAR data. Simple algorithms to produce maps of ice concentration and edge density are outlined and applied to 23.5-cm SAR digital image data