Observations of Lake Baikal ice from satellite altimetry and radiometry

We demonstrate the potential of combining satellite altimetry and radiometry for lake ice studies using the example of Lake Baikal in Siberia. We show the synergy using active and passive microwave observations available from the recent satellite altimetry missions (TOPEX/Poseidon, Jason-1, ENVISAT and Geosat Follow-On), complemented by the SSM/I passive data. We assess the applicability of altimetric and radiometric data for ice/water discrimination, and discuss the drawbacks and benefits of each type of sensor. An ice discrimination method, based on the combined use of the data from the four altimetric missions and SSM/I, is proposed and validated using available in situ observations and MODIS imagery. The method is applied to the entire satellite data set and used to define specific dates of ice events (first appearance of ice, formation of stable ice cover, first appearance of open water, complete disappearance of ice) and associated uncertainties. Using these satellite-derived estimates, we can extend the existing time series of ice events in the Southern Baikal up to 2004 and provide new information on the Middle and Northern Baikal, regions where no recent in situ ice cover observations are available. Our data show that over the last 10-15 years, trends towards earlier ice formation and later ice break-up result in a tendency for longer fast ice duration over the whole Lake Baikal. The methods proposed here have been tested for Lake Baikal, but they are applicable for other lakes and water bodies with seasonal ice cover.     

Locating a sunken vessel using SAR imagery: detection of oil spilled from the SS Jacob Luckenbach

While Radarsat-1 SAR has been used in oil pollution monitoring, it has been mostly used for large spills because using it to differentiate between oil slicks and natural features is difficult. In this investigation, when visual observations failed to pinpoint the source of oil fouling birds off the California coast, Radarsat-1 Synthetic Aperture Radar (SAR) images were analysed using a methodology initially developed by Advanced Resources International, Inc. (ARI) for locating small oil seeps from submerged abandoned wells. Two images contained areas that were consistent with an oil source. Subsequent sighting of oil by recreational divers near a sunken vessel linked the features observed on the imagery to an oil source. The link demonstrates the ability of SAR to detect similar persistent episodes, as well as the validity of the modified ARI approach used.     

The contribution of Seasat to ice sheet glaciology

Abstract

The suite of sensors flown onboard Seasat during 1978 has provided glaciologists with valuable tools for the study of ice masses, particularly in the polar regions. Of the sensor package, the most useful instruments for glaciology have been the radar altimeter and the synthetic aperture radar. The former has demonstrated the ability to map the surface of ice sheets in considerable detail (possibly to better than 50 mm over ice shelves) and over a very short period of time. Such maps provide the first step towards evaluating the long term mass balance of these ice masses. Such studies are of central importance to global climate modelling, investigation of the ‘greenhouse effect’ and prediction of world sea levels. Radar altimeter mapping has also provided unparalleled detail on surface topography relevant to ice dynamics investigations. The small dataset of Seasat Synthetic Aperture Radar (SAR) imagery gathered over ice masses, principally in Iceland and Greenland (there was no coverage of Antarctica), has begun to reveal useful detail of surface and near-surface phenomena such as flowlines, meltwater percolation, and snow and ice facies invaluable for glaciolog-ical reconnaissance. In particular recent studies have shown the value of a multi-sensor approach with the combination of SAR and multi-spectral imagery. It is likely that X- and C-band SARs will prove better for snow and ice discrimination than the L-band system on Seasat. The Scatterometer and Scanning multi-channel microwave radiometer instruments on Seasat have yielded data over ice masses which are still in the early stages of evaluation. Nevertheless there are strong indications of the value of these data for investigation of surface melt phenomena and temperature-accumulation patterns.     

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。     

The role of lake size and local phenomena for monitoring ground-fast lake ice

In this study, we assess the effect of the lake size on the accuracy of a threshold-based classification of ground-fast and floating lake ice from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. For that purpose, two new methods (flood-fill and watershed method) are introduced and the results between the three classification approaches are compared regarding different lake size classes for a study area covering most of the Yamal Peninsula in Western Siberia. The focus is on April, the stage of maximum lake ice thickness, for the years 2016 and 2017. The results indicate that the largest lakes are likely most prone to errors by the threshold classification. The newly introduced methods seem to improve classification results. The results also show differences in fractions of ground-fast lake ice between 2016 and 2017, which might reflect differences in temperatures between the winters with severe impact on wildlife and freshwater fish resources in the region. Patterns of low backscatter responsible for the classification errors in the centre of the lakes were investigated and compared to the optical Sentinel-2 imagery of late-winter. Strong similarities between some patterns in the optical and SAR data were identified. They might be zones of thin ice, but further research is required for clarification of this phenomenon and its causes.     

Ice distribution and winter surface circulation patterns,Kachemak Bay,Alaska

Development of the hydropower potential of Bradley Lake, Alaska, would greatly increase winter freshwater discharge from the Bradley River into Kachemak Bay, which may result in increased ice formation and related ice-induced problems. The objectives of this investigation were to describe winter surface circulation in the bay and document ice distribution patterns for predicting where additional ice might be transported if it forms. Landsat MSS bands 5 and 7 and RBV imagery with 70% cloud cover or less, taken between 1 November and 30 April each year from 1972 to 1980, were analyzed. Surface circulation patterns inferred from suspended sediment patterns and ice distribution and movement were observed and mapped from the Landsat imagery. The generalized circulation patterns indicate that any additional ice formed due to future increased winter discharge from Bradley River would be likely to accumulate along Homer Spit and to be blown into the outer bay by the dominant northerly winter winds.     

On the interpretation of SAR imagery from the Sea Empress oil spill

A method for monitoring oil spills using SAR imagery is suggested, based on the simulation of the wave spectrum using modelled surface winds. A first order separation of the purely wind-driven backscatter distribution and its modification due to surfactant was made by parametrizing the effect of surfactant on the wave growth rate and on the reflective properties of the sea surface. The technique was applied to a SAR image showing the Sea Empress oil spill, in south-west Wales, UK.     

Unsupervised segmentation of ERS and Radarsat sea ice images using multiresolution peak detection and aggregated population equalization

In this paper we describe Automated Sea Ice Segmentation (ASIS), a system that automatically segments Synthetic Aperture Radar (SAR) sea ice imagery. This system integrates image processing, data mining, and machine learning methodologies to determine the number of visually separable classes in ERS and Radarsat sea ice images. We introduce two new techniques: multiresolution peak detection and spatial clustering. The detection is a noise-resistant data discretization methodology that results in an initial segmentation of the image. The clustering is based on an innovative concept called Aggregated Population Equalization that utilizes spatial relationships among classes to merge and split the population environment. Its self-organizing ability produces the final segmentation and automates ASIS. In addition, we have designed a Java-based graphical user interface that facilitates post-segmentation human evaluation and classification. Thus, ASIS can be used as a pre-processor to help analyse sea ice images as well as a basis for human classification of sea ice images. We have tested the system on more than 300 ERS-1, ERS-2 and Radarsat SAR sea ice images and analysed the results to point out the strengths and weaknesses of ASIS in the automated segmentation of sea ice images.     

Classification of sea ice types in the Ross Sea,Antarctica from SAR and AVHRR imagery

ERS-2 synthetic aperture radar (SAR) and Advanced Very High Resolution Radiometer (AVHRR) imagery are used to examine spectral characteristics of late winter/early spring ice in the Ross Sea, Antarctica. The combined spectral signatures are used to distinguish six ice types: fast ice, new ice, smooth first year ice, rough first year ice, thin new ice/wind roughened open water and glacial ice. The procedure firstly involves 'picking' class boundaries from SAR imagery based on the morphology of a speckle reduced backscatter spectrum. These class boundaries are then used as input to an iterative segmentation procedure that involves the repeated application of a speckle reduction filter to the image. For an image from late September 1996 the segmentation procedure enabled separation of five general ice categories each with a characteristic backscatter range. However because of the combined contributions of ice thickness, surface roughness, salinity and water content to the SAR backscatter, further decision criteria are required to separate some physical ice types unable to be resolved individually using this method. Coincident and co-registered infrared data from the AVHRR sensor are used to extract spectral characteristics for the final ice classes. Using this procedure we were able to distinguish floating glacier ice from thin new ice/wind roughened open water and new ice from nearshore fast ice. These ice types were unable to be separated using SAR backscatter intensity alone. In addition image subtraction was also able to clearly delineate areas of shore fast ice.     

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.     

Dark formation detection using neural networks

Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in marine environment, as they are not affected by local weather conditions and cloudiness. Dark formations can be caused by man‐made actions (e.g. oil spills) or natural ocean phenomena (e.g. natural slicks and wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they dampen the capillary and short gravity sea waves. Thus, traditionally, dark formation detection is the first stage of the oil‐spill detection procedure and in most studies is performed manually or using a fixed size window in which a threshold value is adopted. In high‐resolution imagery, dark formation detection may fail due to the nonlinear behaviour of the pixel values contained in the dark formation and in the area around it. In this paper, we examine the ability of two feed‐forward neural network families, i.e. Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks, to detect dark formations in high‐resolution SAR images. The general objective of this paper is to test the potential of artificial neural networks for dark formation detection using SAR high‐resolution satellite images. Both the type and the architecture of the network are subjects of research. The inputs into the networks are the original SAR images. Each network is called to classify an area of the image as dark area or sea. The group of MLP networks can be recognized as the most suitable group for dark formation detection, as it presents reliable stable results for all the examined accuracies. Nevertheless, in terms of single topology, there is no an MLP topology that performs significantly better than the others.     

Multi-sensor remote sensing image change detection based on sorted histograms

Change detection using multi-sensor remote sensing images, such as synthetic aperture radar (SAR) and optical images, is poorly researched and thus remains a challenging task. In this study, we address this problem by proposing a novel automatic change detection method. Different sensors have completely different physical principles. Thus, the resulting multi-sensor images have completely different radiometric values. First, we introduce a sorted histogram concept that sorts the bins in descending order, noticing that multi-sensor images with absence of change have the same combination of objects, and each object in different images has the same proportions and a unique range of grey values. The sorted histogram discards the visual property correspondence between images and is capable of capturing the local internal image layout. Then, various histogram-based distances are employed to estimate the distance between each sorted histogram pair. After the whole image has been analysed, we obtain a divergence index map. The sorted histogram not only has the theoretical advantage of robustness in the intensity variations in multi-sensor images but also the practical advantage of low computational complexity compared with existing methods. Experiments on SAR and optical datasets with different resolutions show promising results in terms of detection capability and run time.     

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions.     

Seasat detection of waves,currents and inlet discharge

Abstract

A new era of remote sensing for coastal and oceanographic monitoring was born on 26 June 1978 with the launch of Seasat. Duck-X was a 2 month experiment conducted during August to October 1978 off the east coast of the U.S.A. for the validation of the Seasat synthetic aperture radar (SAR), During this field experiment, various oceanographic phenomena were monitored. Ground truth observations of these phenomena have been correlated with Seasat SAR imagery. The ground truth sensors included airborne photographic and radar imagery, meteorological satellite imagery, land based radars, and conventional wave gauges. This paper focuses on ocean surface waves, ocean currents and coastal inlet discharge

Specifically, the direction and length of the principal ocean wave trains are compared for the periods of Seasat overflight of the Duck-X area. During these overflights significant wave heights were 1.5 m and less and the maximum wave period was 14 s. The current correlations concentrate on the western boundary of the Gulf Stream and its associated eddy structure. Inlet outflow is shown for inlets on the east coast of the U.S.A

This ground truth study has indicated that the SAR imagery contains an unanticipated abundance of information on a variety of oceanographic and coastal phenomena.     

Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images

Multi-temporal synthetic aperture radar (SAR) interferometric coherence imagery is a useful information source for the detection of random changes of the land surface. Three coherence images derived from three ERS-1 SAR images (acquired on 8 September 1992, 13 October 1992 and 28 September 1993) of a desert area in Algeria revealed several interesting phenomena, including distribution of mobile sand, erosion along river channels, variation of ephemeral lakes and 'mystery' seismic survey lines.     

Model-based interpretation of ERS-1 SAR images of Arctic sea ice

Interpretation of Synthetic Aperture Radar (SAR) images of sea ice is complex because of the natural variability of sea ice and sensor-induced effects, such as speckle. Most of the research on SAR image interpretation has focused on the winter months and algorithms were developed to classify sea ice successfully under cold conditions. However, interpretation of SAR images during the seasonal transitions has proved difficult due to rapidly changing weather conditions. In this paper we address the application of SAR during the transition from summer to the fall freeze-up. This period is important because it signals the start of significant new ice growth, which affects the air-ocean heat exchange and injects brine into the upper layers of the ocean. We have interpreted SAR images of the sea ice in terms of the basic ice characteristics by using shipborne radar measurements of sea ice during the freeze-up and models derived from these measurements. We have shown that the model-based approach is effective in interpreting SAR images during this seasonal transition. This work also provides the physical mechanisms responsible for the large increase in backscatter observed at the end of the summer melt season.     

Multi-sensor synergistic analysis of mesoscale oceanic features: Campos Basin,south-eastern Brazil

This study presents a combined use of multi-sensor remote sensing and in situ data for the analysis and interpretation of oceanic features observed at the continental shelf and slope of the Campos Basin, south-eastern Brazil. Ocean colour (SeaWiFS), thermal infrared (AVHRR), scatterometer winds (QuikSCAT) and SAR (Radarsat-1) data were integrated to associate the different SAR backscatter patterns with physical and biological oceanic processes. The interpreted SAR features included processes such as oceanic fronts, current meandering and eddies, upwelling plumes, wind variability and algae blooms. The interpretation of these features was only feasible through the use of the multi-sensor synergistic approach complemented by timely field verification.     

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。     

Analysing changes of the Poyang Lake water area using Sentinel-1 synthetic aperture radar imagery

Poyang Lake is the largest freshwater lake in China. Monitoring changes of its water area is essential for the conservation of important wetlands and ecological resources, and plays an important role for sustainable water use and management. Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor images are widely used for mapping waterbodies, because of their sensitivity for spectral reflectance of water. However, studies using Landsat images have limited their investigations of changes of Poyang Lake to dry season due to the impairment by cloud cover. Further limited by the rather long 16 day revisit cycle, most existing studies build on the vague assumption that Poyang Lake undergoes only relatively slow changes during this season. MODIS, in contrast, provides a very short revisit period, but has been proven not to be able to assess the water area of Poyang Lake accurately due to low spatial resolution. Therefore, the contribution of this study is to investigate recent Poyang Lake water area changes both during high- and low-water period with unforeseen temporal and spatial resolution using Sentinel-1 synthetic aperture radar (SAR) imagery. More specifically, we aim at investigating Poyang Lake’s recent water area changes in intra-month scales. During the observation period from October 2014 to March 2016, October 2014 was the month with the largest max/min water coverage ratio. Water coverage of winter in 2014 and 2015 was completely different, as a severe drought happened in 2014 and an unusual winter flood happened in 2015. Thus, this study demonstrates the potential of using Sentinel-1 SAR data to reveal intra-month variations, benefiting from the sensor’s regular observation capabilities independent of weather conditions. It is shown that Sentinel-1 SAR data, with rapid availability and free-of-charge distribution policy, as well as relatively high spatial and temporal resolutions, is becoming an indispensable data source for a detailed monitoring of important inland waterbodies and wetlands.     

SAR observations of Arctic freeze-up compared to SSM/I during ARCTIC'91

Ice concentration in the Arctic derived from ERS-1 Synthetic Aperture Radar (SAR) and Special Scanning Microwave/Imager (SSM/I) images are compared. The satellite data are compared to video images and in situ measurements. The data were acquired during the freeze-up period of the ARCTIC'91 expedition. The studied areas were characterized by melting conditions and new ice formation with frost flowers. The ERS-1 SAR images are classified by a local averaging method and a segmentation method. Parameters for the methods are derived from the backscattering distributions. Temporal sequences and meteorological information are used for consistent results. Ice concentration derived from SAR are compared with the SSM/I ice concentration (NASA team algorithm) and ship observations. SSM/I may underestimate the ice concentration by 20 per cent due to thin ice formation and melting conditions while SAR may overestimate. However, by using the SAR estimate of the different ice classes we believe it is possible to increase the accuracy of the NASA team algorithm. We conclude that it is important to compare results from different sensors and methods.